Example development notebook for `app_predict`¶

cd /opt/demo

/opt/demo

# Import standard packages.
import os
import sys
import time
# Import installed packages.
# Import local packages.
sys.path.insert(0, os.path.join(os.path.curdir, r'demo'))
%reload_ext autoreload
%autoreload 2
import demo
%matplotlib inline

/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')
/opt/conda/lib/python3.5/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.
  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')

print("Timestamp:")
print(time.strftime(r'%Y-%m-%dT%H:%M:%S%Z', time.gmtime()))
print()
print("Versions:")
print("Python:", sys.version_info)
print("demo:", demo.__version__)

Timestamp:
2017-03-01T19:29:31GMT

Versions:
Python: sys.version_info(major=3, minor=5, micro=2, releaselevel='final', serial=0)
demo: 0.0.0

!py.test -v --pdb

============================= test session starts ==============================
platform linux -- Python 3.5.2, pytest-2.9.2, py-1.4.31, pluggy-0.3.1 -- /opt/conda/bin/python
cachedir: .cache
rootdir: /opt/demo, inifile: 
collected 8 items 

tests/test__init__.py::test__all__ PASSED
tests/test__init__.py::test__version__ PASSED
tests/test_app_template/test_app_template__init__.py::test__all__ PASSED
tests/test_app_template/test_app_template_main.py::test__all__ PASSED
tests/test_app_template/test_app_template_main.py::test_main PASSED
tests/test_app_template/test_app_template_main.py::test__main__ PASSED
tests/test_app_template/test_template.py::test_prepend_this PASSED
tests/test_utils/test_utils__init__.py::test__all__ PASSED

=========================== 8 passed in 5.56 seconds ===========================

Development below¶

Initialization¶

Imports¶

cd /opt/demo

/opt/demo

# Import standard packages.
import bs4
import collections
import gc
import glob
import io
import itertools
import logging
import os
import pickle
import requests
import shelve
import subprocess
import sys
import textwrap
import time
import warnings
# Import installed packages.
from IPython.display import display, SVG
import geopy
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy
import seaborn as sns
import sklearn as sk
import sklearn.cross_validation as sk_cv
import sklearn.cluster as sk_cl
import sklearn.decomposition as sk_dc
import sklearn.ensemble as sk_ens
import sklearn.metrics as sk_met
import sklearn.preprocessing as sk_pre
import sklearn.tree as sk_tr
# Import local packages.
# datascience version: TODO link to GitHub tag v0.0.1.
sys.path.insert(0, os.path.join(os.path.curdir, r'demo'))
%reload_ext autoreload
%autoreload 2
import demo
%matplotlib inline

print("Timestamp:")
print(time.strftime(r'%Y-%m-%dT%H:%M:%S%Z', time.gmtime()))
print()
print("Versions:")
print("Python:", sys.version_info)
print("matplotlib:", mpl.__version__)
print("numpy:", np.__version__)
print("pandas:", pd.__version__)
print("scipy:", scipy.__version__)
print("sklearn:", sk.__version__)
print("seaborn:", sns.__version__)
print("demo:", demo.__version__)

Timestamp:
2017-03-02T10:00:03GMT

Versions:
Python: sys.version_info(major=3, minor=5, micro=2, releaselevel='final', serial=0)
matplotlib: 1.5.1
numpy: 1.11.1
pandas: 0.18.1
scipy: 0.17.1
sklearn: 0.17.1
seaborn: 0.7.1
demo: 0.0.0

Globals¶

# State settings.
sns.set() # Set matplot lib styles with seaborn
np.random.seed(seed=1) # Set random state for reproducibility.

# File paths
path_data_dir = os.path.join(os.path.abspath(os.path.curdir), r'demo/app_predict/data')
path_csv = os.path.join(path_data_dir, r'Risky Dealer Case Study Transactions.csv')

# Statistics
# percentiles = [-1.6449 sigma, -1 sigma, mean, +1 sigma, +1.6449 sigma] for normal distribution
# 90.00% confidence interval = (percentiles[0], percentiles[-1])
# 68.27% confidence interval = (percentiles[1], percentiles[-2])
# median = percentiles[2]
percentiles = [0.0500, 0.1587, 0.5000, 0.8413, 0.9500]

# Buyer Return Rate.
# Greater than 10% is excessive.
buyer_retrate = 'BuyerID_fracReturned1DivReturnedNotNull'
buyer_retrate_max = 0.1

# Logger
logger = logging.getLogger()

Extract, transform, and load¶

print(r"""`df`: Load and format sales transactions.""")
df_backup = pd.read_csv(path_csv)
df_backup.head()

`df`: Load and format sales transactions.

df = demo.app_predict.predict.etl(df=df_backup)
df = demo.app_predict.predict.create_features(df=df, path_data_dir=path_data_dir)
with pd.option_context('display.max_rows', len(df.dtypes)):
    print(df.dtypes)

BuyerID                                                      object
SellerID                                                     object
Mileage                                                       int64
VIN                                                          object
SellingLocation                                              object
CarMake                                                      object
SalePrice                                                     int64
CarYear                                                       int64
MMR                                                           int64
JDPowersCat                                                  object
LIGHTG                                                        int64
LIGHTY                                                        int64
LIGHTR                                                        int64
PSI                                                           int64
Arbitrated                                                    int64
Salvage                                                       int64
OVE                                                           int64
Simulcast                                                     int64
InLane                                                        int64
PSIEligible                                                   int64
SaleDate                                             datetime64[ns]
Autocheck_score                                             float64
ConditionReport                                               int64
DSEligible                                                    int64
Returned                                                      int64
Returned_asm                                                  int64
SellingLocation_lat                                         float64
SellingLocation_lon                                         float64
JDPowersCat_COMPACTCAR                                      float64
JDPowersCat_EXCLUDED                                        float64
JDPowersCat_FULLSIZECAR                                     float64
JDPowersCat_LUXURYCAR                                       float64
JDPowersCat_MIDSIZECAR                                      float64
JDPowersCat_PICKUP                                          float64
JDPowersCat_SPORTSCAR                                       float64
JDPowersCat_SUV                                             float64
JDPowersCat_UNKNOWN                                         float64
JDPowersCat_VAN                                             float64
LIGHT_N0G1Y2R3                                                int64
SaleDate_dow                                                  int64
SaleDate_doy                                                  int64
SaleDate_day                                                  int64
SaleDate_decyear                                            float64
BuyerID_numTransactions                                       int64
BuyerID_numDSEligible1                                        int64
BuyerID_fracDSEligible1DivTransactions                      float64
BuyerID_numReturnedNotNull                                    int64
BuyerID_fracReturnedNotNullDivDSEligible1                   float64
BuyerID_numReturned1                                          int64
BuyerID_fracReturned1DivReturnedNotNull                     float64
BuyerID_numReturnedasm1                                       int64
BuyerID_fracReturnedasm1DivTransactions                     float64
SellerID_numTransactions                                      int64
SellerID_numDSEligible1                                       int64
SellerID_fracDSEligible1DivTransactions                     float64
SellerID_numReturnedNotNull                                   int64
SellerID_fracReturnedNotNullDivDSEligible1                  float64
SellerID_numReturned1                                         int64
SellerID_fracReturned1DivReturnedNotNull                    float64
SellerID_numReturnedasm1                                      int64
SellerID_fracReturnedasm1DivTransactions                    float64
VIN_numTransactions                                           int64
VIN_numDSEligible1                                            int64
VIN_fracDSEligible1DivTransactions                          float64
VIN_numReturnedNotNull                                        int64
VIN_fracReturnedNotNullDivDSEligible1                       float64
VIN_numReturned1                                              int64
VIN_fracReturned1DivReturnedNotNull                         float64
VIN_numReturnedasm1                                           int64
VIN_fracReturnedasm1DivTransactions                         float64
SellingLocation_numTransactions                               int64
SellingLocation_numDSEligible1                                int64
SellingLocation_fracDSEligible1DivTransactions              float64
SellingLocation_numReturnedNotNull                            int64
SellingLocation_fracReturnedNotNullDivDSEligible1           float64
SellingLocation_numReturned1                                  int64
SellingLocation_fracReturned1DivReturnedNotNull             float64
SellingLocation_numReturnedasm1                               int64
SellingLocation_fracReturnedasm1DivTransactions             float64
CarMake_numTransactions                                       int64
CarMake_numDSEligible1                                        int64
CarMake_fracDSEligible1DivTransactions                      float64
CarMake_numReturnedNotNull                                    int64
CarMake_fracReturnedNotNullDivDSEligible1                   float64
CarMake_numReturned1                                          int64
CarMake_fracReturned1DivReturnedNotNull                     float64
CarMake_numReturnedasm1                                       int64
CarMake_fracReturnedasm1DivTransactions                     float64
JDPowersCat_numTransactions                                   int64
JDPowersCat_numDSEligible1                                    int64
JDPowersCat_fracDSEligible1DivTransactions                  float64
JDPowersCat_numReturnedNotNull                                int64
JDPowersCat_fracReturnedNotNullDivDSEligible1               float64
JDPowersCat_numReturned1                                      int64
JDPowersCat_fracReturned1DivReturnedNotNull                 float64
JDPowersCat_numReturnedasm1                                   int64
JDPowersCat_fracReturnedasm1DivTransactions                 float64
dtype: object

Exploratory data analysis¶

# Describe columns
opt = pd.get_option('display.max_columns')
pd.set_option('display.max_columns', len(df.columns))
df.describe(include='all')

# re-initialize matplotlib inline in case handle dropped
%matplotlib inline
demo.app_predict.predict.plot_eda(
    df=df,
    columns=sorted(df.columns[np.logical_or(df.dtypes=='int64', df.dtypes=='float64')]),
    path_plot_dir=os.path.join(path_data_dir, 'plot_eda'))

################################################################################
Plot frequency distributions (histograms) of columns.
########################################
Feature: Arbitrated
Timestamp: 2017-02-28T07:12:17GMT

########################################
Feature: Autocheck_score
Timestamp: 2017-02-28T07:12:19GMT

########################################
Feature: BuyerID_fracDSEligible1DivTransactions
Timestamp: 2017-02-28T07:12:21GMT

########################################
Feature: BuyerID_fracReturned1DivReturnedNotNull
Timestamp: 2017-02-28T07:12:23GMT

########################################
Feature: BuyerID_fracReturnedNotNullDivDSEligible1
Timestamp: 2017-02-28T07:12:25GMT

########################################
Feature: BuyerID_fracReturnedasm1DivTransactions
Timestamp: 2017-02-28T07:12:27GMT

########################################
Feature: BuyerID_numDSEligible1
Timestamp: 2017-02-28T07:12:29GMT

########################################
Feature: BuyerID_numReturned1
Timestamp: 2017-02-28T07:12:31GMT

########################################
Feature: BuyerID_numReturnedNotNull
Timestamp: 2017-02-28T07:12:33GMT

########################################
Feature: BuyerID_numReturnedasm1
Timestamp: 2017-02-28T07:12:35GMT

print(textwrap.dedent("""\
    Cluster map of feature correlations with heirarchical relationships.
    The deeper of the dendrogram node, the higher (anti)correlated the features are.
    The Spearman rank correlation accommodates non-linear features.
    The pair plot is a scatter matrix plot of columns vs each other."""))

# Write column labels to csv file.
path_plot_dir = os.path.join(path_data_dir, 'plot_eda_extra')
cols_plot = df.columns[np.logical_or(df.dtypes=='int64', df.dtypes=='float64')]
ds_columns = pd.Series(cols_plot, name='column')
ds_columns.to_csv(
    os.path.join(path_plot_dir, 'eda-extra_index_column_map.csv'),
    header=True, index_label='index')

# Create clustermap.
df_tmp = df[cols_plot].sample(n=1000, replace=False, random_state=0)
df_tmp.columns = [np.where(ds_columns.values == col)[0][0] for col in df_tmp.columns]
cmap = sns.clustermap(df_tmp.corr(method='spearman'), figsize=(20, 20))
plt.savefig(
    os.path.join(path_plot_dir, 'eda-extra_clustermap.png'),
    bbox_inches='tight', dpi=300)
plt.show()

# Print and write ordered column labels to csv file
with pd.option_context('display.max_rows', len(cmap.data2d.columns)):
    print(ds_columns.loc[cmap.data2d.columns])
ds_columns.loc[cmap.data2d.columns].to_csv(
    os.path.join(path_plot_dir, 'eda-extra_clustermap_index_column_map.csv'),
    header=True, index_label='index')

Cluster map of feature correlations with heirarchical relationships.
The deeper of the dendrogram node, the higher (anti)correlated the features are.
The Spearman rank correlation accommodates non-linear features.
The pair plot is a scatter matrix plot of columns vs each other.

55                                   VIN_numDSEligible1
16                                           DSEligible
56                   VIN_fracDSEligible1DivTransactions
38               BuyerID_fracDSEligible1DivTransactions
13                                          PSIEligible
2                                               CarYear
1                                             SalePrice
3                                                   MMR
47              SellerID_fracDSEligible1DivTransactions
65       SellingLocation_fracDSEligible1DivTransactions
28                                      JDPowersCat_SUV
83           JDPowersCat_fracDSEligible1DivTransactions
12                                               InLane
4                                                LIGHTG
15                                      ConditionReport
24                                JDPowersCat_LUXURYCAR
74               CarMake_fracDSEligible1DivTransactions
25                               JDPowersCat_MIDSIZECAR
10                                                  OVE
19                                  SellingLocation_lat
20                                  SellingLocation_lon
7                                                   PSI
14                                      Autocheck_score
11                                            Simulcast
32                                         SaleDate_dow
21                               JDPowersCat_COMPACTCAR
26                                   JDPowersCat_PICKUP
5                                                LIGHTY
29                                  JDPowersCat_UNKNOWN
34                                         SaleDate_day
27                                JDPowersCat_SPORTSCAR
30                                      JDPowersCat_VAN
22                                 JDPowersCat_EXCLUDED
23                              JDPowersCat_FULLSIZECAR
80              CarMake_fracReturnedasm1DivTransactions
79                              CarMake_numReturnedasm1
72                              CarMake_numTransactions
73                               CarMake_numDSEligible1
41                                 BuyerID_numReturned1
42              BuyerID_fracReturned1DivReturnedNotNull
39                           BuyerID_numReturnedNotNull
40            BuyerID_fracReturnedNotNullDivDSEligible1
78              CarMake_fracReturned1DivReturnedNotNull
87          JDPowersCat_fracReturned1DivReturnedNotNull
81                          JDPowersCat_numTransactions
82                           JDPowersCat_numDSEligible1
75                           CarMake_numReturnedNotNull
77                                 CarMake_numReturned1
76            CarMake_fracReturnedNotNullDivDSEligible1
85        JDPowersCat_fracReturnedNotNullDivDSEligible1
33                                         SaleDate_doy
35                                     SaleDate_decyear
88                          JDPowersCat_numReturnedasm1
84                       JDPowersCat_numReturnedNotNull
86                             JDPowersCat_numReturned1
69      SellingLocation_fracReturned1DivReturnedNotNull
68                         SellingLocation_numReturned1
66                   SellingLocation_numReturnedNotNull
67    SellingLocation_fracReturnedNotNullDivDSEligible1
70                      SellingLocation_numReturnedasm1
63                      SellingLocation_numTransactions
64                       SellingLocation_numDSEligible1
50                                SellerID_numReturned1
51             SellerID_fracReturned1DivReturnedNotNull
48                          SellerID_numReturnedNotNull
49           SellerID_fracReturnedNotNullDivDSEligible1
36                              BuyerID_numTransactions
37                               BuyerID_numDSEligible1
45                             SellerID_numTransactions
46                              SellerID_numDSEligible1
9                                               Salvage
18                                         Returned_asm
61                                  VIN_numReturnedasm1
62                  VIN_fracReturnedasm1DivTransactions
31                                       LIGHT_N0G1Y2R3
0                                               Mileage
6                                                LIGHTR
59                                     VIN_numReturned1
60                  VIN_fracReturned1DivReturnedNotNull
17                                             Returned
57                               VIN_numReturnedNotNull
58                VIN_fracReturnedNotNullDivDSEligible1
8                                            Arbitrated
54                                  VIN_numTransactions
89          JDPowersCat_fracReturnedasm1DivTransactions
43                              BuyerID_numReturnedasm1
44              BuyerID_fracReturnedasm1DivTransactions
71      SellingLocation_fracReturnedasm1DivTransactions
52                             SellerID_numReturnedasm1
53             SellerID_fracReturnedasm1DivTransactions
Name: column, dtype: object

print(textwrap.dedent("""\
    Cluster map of record correlations with heirarchical relationships."""))

# Create clustermap.
sns.clustermap(df_tmp.transpose().corr(method='spearman'), figsize=(20, 20))
plt.savefig(
    os.path.join(path_plot_dir, 'eda-extra_clustermap_records.png'),
    bbox_inches='tight', dpi=300)
plt.show()

Cluster map of record correlations with heirarchical relationships.

print("Pairplot of columns (ordered like clustermap):")
buyer_retrate_idx = np.where(ds_columns.values == buyer_retrate)[0][0]
idxs_top_corr = cmap.data2d[buyer_retrate_idx].abs().sort_values(ascending=False)[:6].index
tfmask_top_corr = cmap.data2d.index.isin(idxs_top_corr)
col_hue = 'buyer_retrate_gt01'
cols_plot = cmap.data2d.columns[tfmask_top_corr].append(pd.Index([col_hue]))
df_tmp[col_hue] = df_tmp[buyer_retrate_idx] > buyer_retrate_max

sns.pairplot(
    df_tmp[cols_plot], hue=col_hue, diag_kind='hist', markers=['.', 'o'],
    palette=[sns.color_palette()[1], sns.color_palette()[2]],
    plot_kws={'alpha':1.0})
plt.savefig(
    os.path.join(path_plot_dir, 'eda-extra_pairplot.png'),
    bbox_inches='tight', dpi=300)
plt.show()

Pairplot of columns (ordered like clustermap):

del df_tmp
gc.collect()

233416

Heuristic model¶

# re-initialize matplotlib inline in case handle dropped
%matplotlib inline
demo.app_predict.predict.plot_heuristic(
    df=df,
    path_plot_dir=os.path.join(path_data_dir, 'plot_heuristic'))

Test heuristic¶

Note: The heuristic isn't "trained", just calculated (no iterations). TODO:

Bootstrap to get uncertainties.
Make list of bad dealers
Predict 'Returned': P(Returned < 0.1) then P(Dealer_retrate < 0.1)
Don't remove Returned == -1 since that means not DealShield approved or yes DealShield approved but not purchased.

date_range = pd.date_range(start=df['SaleDate'].min(), end=df['SaleDate'].max(), freq='W')
df_modl = df.loc[
    np.logical_and(
        date_range[0] <= df['SaleDate'],
        df['SaleDate'] < date_range[1])].copy()
df_orig = df.loc[
    np.logical_and(
        date_range[0] <= df['SaleDate'],
        df['SaleDate'] < date_range[1])].copy()
buyers = np.asarray([])
buyers_prohibited = dict() # key: (df_train['SaleDate'].min(), df_train['SaleDate'].max())
transactions_affected = dict() # key: (df_test['SaleDate'].min(), df_test['SaleDate'].max())
retrates_modl_chunk = dict() # key: (df_test['SaleDate'].min(), df_test['SaleDate'].max())
retrates_orig_chunk = dict() # key: (df_eval['SaleDate'].min(), df_eval['SaleDate'].max())
retrates_modl_all = dict() # key: (df_modl['SaleDate'].min(), df_modl['SaleDate'].max())
retrates_orig_all = dict() # key: (df_orig['SaleDate'].min(), df_orig['SaleDate'].max())

for idx in range(len(date_range)-2):
    print('#'*40)
    print('Timestamp:', time.strftime(r'%Y-%m-%dT%H:%M:%S%Z', time.gmtime()))
    
    # Define data sets.
    (saledate_train_min, saledate_train_max) = (date_range[idx],   date_range[idx+1])
    (saledate_test_min,  saledate_test_max)  = (date_range[idx+1], date_range[idx+2])
    (saledate_eval_min,  saledate_eval_max)  = (date_range[idx+1], date_range[idx+2])
    df_train = df_modl.loc[
        np.logical_and(
            saledate_train_min <= df['SaleDate'],
            df['SaleDate'] < saledate_train_max)].copy()
    df_test = df.loc[
        np.logical_and(
            saledate_test_min <= df['SaleDate'],
            df['SaleDate'] < saledate_test_max)].copy()
    df_eval = df.loc[
        np.logical_and(
            saledate_eval_min <= df['SaleDate'],
            df['SaleDate'] < saledate_eval_max)].copy()
    
    # Prohibit purchase of DealShield in test data:
    # If prohibiting purchase of DealShield,
    # then for prohibed buyers, set DSEligible = 0 and Returned = -1
    buyers = np.unique(
        np.append(
            buyers,
            df_train.loc[df_train[buyer_retrate] > buyer_retrate_max, 'BuyerID'].unique()))
    buyers_prohibited[(df_train['SaleDate'].min(), df_train['SaleDate'].max())] = buyers
    tfmask = np.logical_and(
        np.logical_and(
            df_test['SaleDate'] > df_train['SaleDate'].max(),
            df_test['BuyerID'].isin(buyers)),
        df_test['DSEligible'] == 1)
    transactions_affected[(df_test['SaleDate'].min(), df_test['SaleDate'].max())] = tfmask.loc[tfmask].index
    df_test.loc[tfmask, 'DSEligible'] = 0
    df_test.loc[tfmask, 'Returned'] = -1
    
    # Update calculated features.
    df_modl = demo.app_predict.predict.update_features_append(
        df_prev=df_modl, df_next=df_test, debug=False)
    df_orig = df_orig.append(df_eval)
    
    # Calculate chunk return rates.
    df_chunk = df_modl.loc[
        np.logical_and(
            saledate_test_min <= df_modl['SaleDate'],
            df_modl['SaleDate'] < saledate_test_max)]
    retrate_modl = sum(df_chunk['Returned']==1)/sum(df_chunk['Returned']!=-1)
    df_chunk = df_orig.loc[
        np.logical_and(
            saledate_eval_min <= df_orig['SaleDate'],
            df_orig['SaleDate'] < saledate_eval_max)]
    retrate_orig = sum(df_chunk['Returned']==1)/sum(df_chunk['Returned']!=-1)
    retrates_modl_chunk[(saledate_test_min, saledate_test_max)] = retrate_modl
    retrates_orig_chunk[(saledate_eval_min, saledate_eval_max)] = retrate_orig
    print('Chunk:')
    print('Evaluation time span:', df_chunk['SaleDate'].min(), df_chunk['SaleDate'].max())
    print('Model return rate:   ', retrate_modl)
    print('Original return rate:', retrate_orig)

    # Calculate overall return rates.
    retrate_modl = sum(df_modl['Returned']==1)/sum(df_modl['Returned']!=-1)
    retrate_orig = sum(df_orig['Returned']==1)/sum(df_orig['Returned']!=-1)
    retrates_modl_all[(df_modl['SaleDate'].min(), df_modl['SaleDate'].max())] = retrate_modl
    retrates_orig_all[(df_orig['SaleDate'].min(), df_orig['SaleDate'].max())] = retrate_orig
    print('Overall:')
    print('Evaluation time span:', df_orig['SaleDate'].min(), df_orig['SaleDate'].max())
    print('Model return rate:   ', retrate_modl)
    print('Original return rate:', retrate_orig)

########################################
Timestamp: 2017-02-28T19:27:11GMT
Chunk:
Evaluation time span: 2013-02-10 00:00:00 2013-02-16 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-02-16 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-02-28T19:27:12GMT
Chunk:
Evaluation time span: 2013-02-17 00:00:00 2013-02-23 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-02-23 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-02-28T19:27:12GMT
Chunk:
Evaluation time span: 2013-02-24 00:00:00 2013-03-02 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-02 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-02-28T19:27:13GMT
Chunk:
Evaluation time span: 2013-03-03 00:00:00 2013-03-09 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-09 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-02-28T19:27:14GMT
Chunk:
Evaluation time span: 2013-03-10 00:00:00 2013-03-16 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-16 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-02-28T19:27:16GMT
Chunk:
Evaluation time span: 2013-03-17 00:00:00 2013-03-23 00:00:00
Model return rate:    0.0
Original return rate: 0.0
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-23 00:00:00
Model return rate:    0.0
Original return rate: 0.0
########################################
Timestamp: 2017-02-28T19:27:17GMT
Chunk:
Evaluation time span: 2013-03-25 00:00:00 2013-03-30 00:00:00
Model return rate:    0.0
Original return rate: 0.0
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-30 00:00:00
Model return rate:    0.0
Original return rate: 0.0
########################################
Timestamp: 2017-02-28T19:27:18GMT
Chunk:
Evaluation time span: 2013-03-31 00:00:00 2013-04-06 00:00:00
Model return rate:    0.0263157894737
Original return rate: 0.0263157894737
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-06 00:00:00
Model return rate:    0.02
Original return rate: 0.02
########################################
Timestamp: 2017-02-28T19:27:20GMT
Chunk:
Evaluation time span: 2013-04-07 00:00:00 2013-04-13 00:00:00
Model return rate:    0.0
Original return rate: 0.0
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-13 00:00:00
Model return rate:    0.0103092783505
Original return rate: 0.0103092783505
########################################
Timestamp: 2017-02-28T19:27:22GMT
Chunk:
Evaluation time span: 2013-04-14 00:00:00 2013-04-19 00:00:00
Model return rate:    0.0545454545455
Original return rate: 0.0535714285714
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-19 00:00:00
Model return rate:    0.0263157894737
Original return rate: 0.0261437908497
########################################
Timestamp: 2017-02-28T19:27:23GMT
Chunk:
Evaluation time span: 2013-04-21 00:00:00 2013-04-27 00:00:00
Model return rate:    0.0588235294118
Original return rate: 0.0576923076923
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-27 00:00:00
Model return rate:    0.0344827586207
Original return rate: 0.0341463414634
########################################
Timestamp: 2017-02-28T19:27:25GMT
Chunk:
Evaluation time span: 2013-04-28 00:00:00 2013-05-04 00:00:00
Model return rate:    0.0816326530612
Original return rate: 0.0816326530612
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-04 00:00:00
Model return rate:    0.0436507936508
Original return rate: 0.0433070866142
########################################
Timestamp: 2017-02-28T19:27:28GMT
Chunk:
Evaluation time span: 2013-05-05 00:00:00 2013-05-11 00:00:00
Model return rate:    0.281481481481
Original return rate: 0.274647887324
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-11 00:00:00
Model return rate:    0.12661498708
Original return rate: 0.126262626263
########################################
Timestamp: 2017-02-28T19:27:29GMT
Chunk:
Evaluation time span: 2013-05-12 00:00:00 2013-05-18 00:00:00
Model return rate:    0.2625
Original return rate: 0.269662921348
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-18 00:00:00
Model return rate:    0.149892933619
Original return rate: 0.152577319588
########################################
Timestamp: 2017-02-28T19:27:31GMT
Chunk:
Evaluation time span: 2013-05-19 00:00:00 2013-05-25 00:00:00
Model return rate:    0.168604651163
Original return rate: 0.185567010309
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-25 00:00:00
Model return rate:    0.154929577465
Original return rate: 0.162002945508
########################################
Timestamp: 2017-02-28T19:27:34GMT
Chunk:
Evaluation time span: 2013-05-26 00:00:00 2013-05-31 00:00:00
Model return rate:    0.263157894737
Original return rate: 0.298780487805
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-31 00:00:00
Model return rate:    0.173575129534
Original return rate: 0.188612099644
########################################
Timestamp: 2017-02-28T19:27:36GMT
Chunk:
Evaluation time span: 2013-06-02 00:00:00 2013-06-08 00:00:00
Model return rate:    0.335766423358
Original return rate: 0.30243902439
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-08 00:00:00
Model return rate:    0.19801980198
Original return rate: 0.210877862595
########################################
Timestamp: 2017-02-28T19:27:38GMT
Chunk:
Evaluation time span: 2013-06-10 00:00:00 2013-06-15 00:00:00
Model return rate:    0.286516853933
Original return rate: 0.278195488722
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-15 00:00:00
Model return rate:    0.21251149954
Original return rate: 0.224505327245
########################################
Timestamp: 2017-02-28T19:27:41GMT
Chunk:
Evaluation time span: 2013-06-16 00:00:00 2013-06-22 00:00:00
Model return rate:    0.106194690265
Original return rate: 0.166153846154
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-22 00:00:00
Model return rate:    0.194211728865
Original return rate: 0.21293471629
########################################
Timestamp: 2017-02-28T19:27:44GMT
Chunk:
Evaluation time span: 2013-06-23 00:00:00 2013-06-29 00:00:00
Model return rate:    0.187311178248
Original return rate: 0.203187250996
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-29 00:00:00
Model return rate:    0.192822384428
Original return rate: 0.210649229332
########################################
Timestamp: 2017-02-28T19:27:47GMT
Chunk:
Evaluation time span: 2013-06-30 00:00:00 2013-07-06 00:00:00
Model return rate:    0.166666666667
Original return rate: 0.220708446866
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-06 00:00:00
Model return rate:    0.189345991561
Original return rate: 0.212121212121
########################################
Timestamp: 2017-02-28T19:27:50GMT
Chunk:
Evaluation time span: 2013-07-07 00:00:00 2013-07-13 00:00:00
Model return rate:    0.185915492958
Original return rate: 0.2
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-13 00:00:00
Model return rate:    0.188804975566
Original return rate: 0.20987654321
########################################
Timestamp: 2017-02-28T19:27:53GMT
Chunk:
Evaluation time span: 2013-07-14 00:00:00 2013-07-20 00:00:00
Model return rate:    0.189102564103
Original return rate: 0.244698205546
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-20 00:00:00
Model return rate:    0.188841201717
Original return rate: 0.215659712815
########################################
Timestamp: 2017-02-28T19:27:57GMT
Chunk:
Evaluation time span: 2013-07-21 00:00:00 2013-07-27 00:00:00
Model return rate:    0.22027972028
Original return rate: 0.269097222222
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-27 00:00:00
Model return rate:    0.191997191997
Original return rate: 0.22287321303
########################################
Timestamp: 2017-02-28T19:28:00GMT
Chunk:
Evaluation time span: 2013-07-28 00:00:00 2013-08-03 00:00:00
Model return rate:    0.219594594595
Original return rate: 0.256944444444
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-03 00:00:00
Model return rate:    0.194594594595
Original return rate: 0.226925459426
########################################
Timestamp: 2017-02-28T19:28:04GMT
Chunk:
Evaluation time span: 2013-08-04 00:00:00 2013-08-10 00:00:00
Model return rate:    0.232067510549
Original return rate: 0.252727272727
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-10 00:00:00
Model return rate:    0.197220579539
Original return rate: 0.229556832932
########################################
Timestamp: 2017-02-28T19:28:08GMT
Chunk:
Evaluation time span: 2013-08-11 00:00:00 2013-08-17 00:00:00
Model return rate:    0.164086687307
Original return rate: 0.230529595016
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-17 00:00:00
Model return rate:    0.194331983806
Original return rate: 0.22966031483
########################################
Timestamp: 2017-02-28T19:28:11GMT
Chunk:
Evaluation time span: 2013-08-18 00:00:00 2013-08-24 00:00:00
Model return rate:    0.12962962963
Original return rate: 0.184568835098
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-24 00:00:00
Model return rate:    0.189128816083
Original return rate: 0.225209080048
########################################
Timestamp: 2017-02-28T19:28:15GMT
Chunk:
Evaluation time span: 2013-08-25 00:00:00 2013-08-31 00:00:00
Model return rate:    0.152941176471
Original return rate: 0.225543478261
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-31 00:00:00
Model return rate:    0.186312657359
Original return rate: 0.22524219591
########################################
Timestamp: 2017-02-28T19:28:19GMT
Chunk:
Evaluation time span: 2013-09-01 00:00:00 2013-09-07 00:00:00
Model return rate:    0.146179401993
Original return rate: 0.193602693603
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-07 00:00:00
Model return rate:    0.183725910064
Original return rate: 0.222900573137
########################################
Timestamp: 2017-02-28T19:28:23GMT
Chunk:
Evaluation time span: 2013-09-08 00:00:00 2013-09-14 00:00:00
Model return rate:    0.164864864865
Original return rate: 0.221621621622
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-14 00:00:00
Model return rate:    0.182341269841
Original return rate: 0.222792607803
########################################
Timestamp: 2017-02-28T19:28:29GMT
Chunk:
Evaluation time span: 2013-09-15 00:00:00 2013-09-21 00:00:00
Model return rate:    0.165413533835
Original return rate: 0.235378031384
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-21 00:00:00
Model return rate:    0.18149264983
Original return rate: 0.223724516742
########################################
Timestamp: 2017-02-28T19:28:36GMT
Chunk:
Evaluation time span: 2013-09-22 00:00:00 2013-09-28 00:00:00
Model return rate:    0.107023411371
Original return rate: 0.198550724638
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-28 00:00:00
Model return rate:    0.177520071365
Original return rate: 0.222014374323
########################################
Timestamp: 2017-02-28T19:28:41GMT
Chunk:
Evaluation time span: 2013-09-29 00:00:00 2013-10-05 00:00:00
Model return rate:    0.134375
Original return rate: 0.236694677871
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-05 00:00:00
Model return rate:    0.175189873418
Original return rate: 0.222978566829
########################################
Timestamp: 2017-02-28T19:28:47GMT
Chunk:
Evaluation time span: 2013-10-06 00:00:00 2013-10-12 00:00:00
Model return rate:    0.183946488294
Original return rate: 0.260740740741
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-12 00:00:00
Model return rate:    0.175610539846
Original return rate: 0.225186211675
########################################
Timestamp: 2017-02-28T19:28:55GMT
Chunk:
Evaluation time span: 2013-10-13 00:00:00 2013-10-19 00:00:00
Model return rate:    0.164658634538
Original return rate: 0.280575539568
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-19 00:00:00
Model return rate:    0.175189247644
Original return rate: 0.228331018708
########################################
Timestamp: 2017-02-28T19:29:01GMT
Chunk:
Evaluation time span: 2013-10-20 00:00:00 2013-10-26 00:00:00
Model return rate:    0.174418604651
Original return rate: 0.238993710692
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-26 00:00:00
Model return rate:    0.175150359396
Original return rate: 0.228981282602
########################################
Timestamp: 2017-02-28T19:29:07GMT
Chunk:
Evaluation time span: 2013-10-27 00:00:00 2013-11-02 00:00:00
Model return rate:    0.171662125341
Original return rate: 0.236220472441
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-02 00:00:00
Model return rate:    0.174972160356
Original return rate: 0.22938106972
########################################
Timestamp: 2017-02-28T19:29:14GMT
Chunk:
Evaluation time span: 2013-11-03 00:00:00 2013-11-09 00:00:00
Model return rate:    0.125944584383
Original return rate: 0.179761904762
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-09 00:00:00
Model return rate:    0.17240469595
Original return rate: 0.226533679464
########################################
Timestamp: 2017-02-28T19:29:21GMT
Chunk:
Evaluation time span: 2013-11-10 00:00:00 2013-11-16 00:00:00
Model return rate:    0.114525139665
Original return rate: 0.178717598909
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-16 00:00:00
Model return rate:    0.169794684469
Original return rate: 0.224253464316
########################################
Timestamp: 2017-02-28T19:29:28GMT
Chunk:
Evaluation time span: 2013-11-17 00:00:00 2013-11-23 00:00:00
Model return rate:    0.124324324324
Original return rate: 0.183979974969
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-23 00:00:00
Model return rate:    0.167769888073
Original return rate: 0.222263450835
########################################
Timestamp: 2017-02-28T19:29:36GMT
Chunk:
Evaluation time span: 2013-11-24 00:00:00 2013-11-30 00:00:00
Model return rate:    0.0877192982456
Original return rate: 0.181818181818
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-30 00:00:00
Model return rate:    0.166155660377
Original return rate: 0.221296631655
########################################
Timestamp: 2017-02-28T19:29:43GMT
Chunk:
Evaluation time span: 2013-12-01 00:00:00 2013-12-07 00:00:00
Model return rate:    0.105849582173
Original return rate: 0.175675675676
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-07 00:00:00
Model return rate:    0.163706301618
Original return rate: 0.219345891598
########################################
Timestamp: 2017-02-28T19:29:51GMT
Chunk:
Evaluation time span: 2013-12-09 00:00:00 2013-12-14 00:00:00
Model return rate:    0.0692124105012
Original return rate: 0.170186335404
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-14 00:00:00
Model return rate:    0.159429682437
Original return rate: 0.217160841478
########################################
Timestamp: 2017-02-28T19:29:59GMT
Chunk:
Evaluation time span: 2013-12-15 00:00:00 2013-12-20 00:00:00
Model return rate:    0.061135371179
Original return rate: 0.144144144144
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-20 00:00:00
Model return rate:    0.154796212433
Original return rate: 0.212944175641
########################################
Timestamp: 2017-02-28T19:30:07GMT
Chunk:
Evaluation time span: 2013-12-22 00:00:00 2013-12-28 00:00:00
Model return rate:    0.0645161290323
Original return rate: 0.142857142857
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-28 00:00:00
Model return rate:    0.153940258946
Original return rate: 0.212037179685

# Overall return rate is Returned==1 / (Returned not null)
print("Plot performance of heuristic")
path_plot_dir = os.path.join(path_data_dir, 'plot_performance_heuristic')

# Plot overall return rate.
(xvals_orig, yvals_orig) = zip(*[(key[1], val) for (key, val) in sorted(retrates_orig_all.items())])
(xvals_modl, yvals_modl) = zip(*[(key[1], val) for (key, val) in sorted(retrates_modl_all.items())])
(xvals_diff, yvals_diff) = (xvals_orig, np.subtract(yvals_modl, yvals_orig))
plt.plot(
    xvals_orig, yvals_orig, marker='.', linestyle='-',
    color=sns.color_palette()[0], label='original return rate')
plt.plot(
    xvals_modl, yvals_modl, marker='.', linestyle='-',
    color=sns.color_palette()[1], label='model return rate')
plt.plot(
    xvals_diff, yvals_diff, marker='.', linestyle='--',
    color=sns.color_palette()[3], label='diff model-original')
plt.title("Return rates vs SaleDate")
plt.xlabel("SaleDate")
plt.ylabel("Overall return rate")
plt.legend(loc='upper left')
plt.tight_layout()
plt.savefig(
    os.path.join(path_plot_dir, 'perf-heur_returnrate_vs_saledate.png'),
    dpi=300)
plt.show()

# Plot return rate by buyer.
# Note: to get buyerid with col max: df_orig.loc[df_orig[col].argmax(), 'BuyerID']
buyerids = [
    '272356', # buyer with max BuyerID_numReturnedNotNull (max accepted DealShield)
    '328701', # buyer with max BuyerID_numReturned1 (max returns)
    '179863', # buyer with max BuyerID_fracReturned1DivReturnedNotNull (max return rate) and frequenly prohibited (36 weeks)
    '46857', # buyers frequently prohibited (38 weeks)
    '62851', # buyers frequently prohibited (36 weeks)
    '16640', # buyers frequently prohibited (36 weeks)
    '61773', # buyers frequently prohibited (35 weeks)
    '20718', # buyers frequently prohibited (35 weeks)
    '18584', # buyers frequently prohibited (34 weeks)
    '248009'] # buyers frequently prohibited (34 weeks)

for buyerid in buyerids:
    print('#'*40)
    print('BuyerID:', buyerid)

    fig = plt.figure()
    ax0 = fig.add_subplot(111)
    ax0.set_title(textwrap.dedent("""\
        Returns and return rates vs num transactions
        for BuyerID={buyerid}""".format(buyerid=buyerid)))
    ax0.set_xlabel('BuyerID_numTransactions')

    ax0.set_ylabel('BuyerID_numReturned1')
    lns0 = list()
    xvals_orig = df_orig.loc[df_orig['BuyerID']==buyerid, 'BuyerID_numTransactions'].values
    yvals0_orig = df_orig.loc[df_orig['BuyerID']==buyerid, 'BuyerID_numReturned1'].values
    lns0 += ax0.plot(
        xvals_orig, yvals0_orig, marker='.', linestyle='-', 
        color=sns.color_palette(palette='dark')[0], label='original BuyerID_numReturned1')
    xvals_modl = df_modl.loc[df_modl['BuyerID']==buyerid, 'BuyerID_numTransactions'].values
    yvals0_modl = df_modl.loc[df_modl['BuyerID']==buyerid, 'BuyerID_numReturned1'].values
    lns0 += ax0.plot(
        xvals_modl, yvals0_modl, marker='.', linestyle='-', 
        color=sns.color_palette(palette=None)[0], label='model BuyerID_numReturned1')
    ylim0 = (
        min(min(yvals0_orig), min(yvals0_modl)),
        max(max(max(yvals0_orig), max(yvals0_modl)), 1))
    ax0.set_ylim(ylim0)
    nticks = 6
    ax0.set_yticks(np.linspace(start=ylim0[0], stop=ylim0[1], num=nticks, endpoint=True))
    ax0.legend(lns0, [ln.get_label() for ln in lns0], loc='upper left')

    ax1 = ax0.twinx()
    ax1.set_ylabel(buyer_retrate)
    lns1 = list()
    yvals1_orig = df_orig.loc[df_orig['BuyerID']==buyerid, buyer_retrate].values
    lns1 += ax1.plot(
        xvals_orig, yvals1_orig, marker='.', linestyle='-', 
        color=sns.color_palette(palette='dark')[1], label='original '+buyer_retrate)
    yvals1_modl = df_modl.loc[df_modl['BuyerID']==buyerid, buyer_retrate].values
    lns1 += ax1.plot(
        xvals_modl, yvals1_modl, marker='.', linestyle='-', 
        color=sns.color_palette(palette=None)[1], label='model '+buyer_retrate)
    ylim1 = (0, 1)
    ax1.set_ylim(ylim1)
    ax1.set_yticks(np.linspace(start=ylim1[0], stop=ylim1[1], num=nticks, endpoint=True))
    ax1.legend(lns1, [ln.get_label() for ln in lns1], loc='lower right')
    plt.tight_layout()
    plt.savefig(
        os.path.join(path_plot_dir, 'perf-heur_returnrate_vs_transactions_for_'+buyerid+'.png'),
        dpi=300)
    plt.show(fig)

Plot performance of heuristic

########################################
BuyerID: 272356

########################################
BuyerID: 328701

########################################
BuyerID: 179863

########################################
BuyerID: 46857

########################################
BuyerID: 62851

########################################
BuyerID: 16640

########################################
BuyerID: 61773

/opt/conda/lib/python3.5/site-packages/matplotlib/axes/_base.py:3045: UserWarning: Attempting to set identical bottom==top results
in singular transformations; automatically expanding.
bottom=1, top=1
  'bottom=%s, top=%s') % (bottom, top))

########################################
BuyerID: 20718

del df_chunk, df_eval, df_modl, df_orig, df_test, df_train
gc.collect()

Pipeline model¶

TODO:

Try with data subset DSEligible==1, then use LDA instead of PCA to create the features to separate Returned=1/0

# Define target and features
target = 'Returned'
features = set(df.columns[np.logical_or(df.dtypes=='int64', df.dtypes=='float64')])
features.difference_update([target])
features = sorted(features)
features

['Arbitrated',
 'Autocheck_score',
 'BuyerID_fracDSEligible1DivTransactions',
 'BuyerID_fracReturned1DivReturnedNotNull',
 'BuyerID_fracReturnedNotNullDivDSEligible1',
 'BuyerID_fracReturnedasm1DivTransactions',
 'BuyerID_numDSEligible1',
 'BuyerID_numReturned1',
 'BuyerID_numReturnedNotNull',
 'BuyerID_numReturnedasm1',
 'BuyerID_numTransactions',
 'CarMake_fracDSEligible1DivTransactions',
 'CarMake_fracReturned1DivReturnedNotNull',
 'CarMake_fracReturnedNotNullDivDSEligible1',
 'CarMake_fracReturnedasm1DivTransactions',
 'CarMake_numDSEligible1',
 'CarMake_numReturned1',
 'CarMake_numReturnedNotNull',
 'CarMake_numReturnedasm1',
 'CarMake_numTransactions',
 'CarYear',
 'ConditionReport',
 'DSEligible',
 'InLane',
 'JDPowersCat_COMPACTCAR',
 'JDPowersCat_EXCLUDED',
 'JDPowersCat_FULLSIZECAR',
 'JDPowersCat_LUXURYCAR',
 'JDPowersCat_MIDSIZECAR',
 'JDPowersCat_PICKUP',
 'JDPowersCat_SPORTSCAR',
 'JDPowersCat_SUV',
 'JDPowersCat_UNKNOWN',
 'JDPowersCat_VAN',
 'JDPowersCat_fracDSEligible1DivTransactions',
 'JDPowersCat_fracReturned1DivReturnedNotNull',
 'JDPowersCat_fracReturnedNotNullDivDSEligible1',
 'JDPowersCat_fracReturnedasm1DivTransactions',
 'JDPowersCat_numDSEligible1',
 'JDPowersCat_numReturned1',
 'JDPowersCat_numReturnedNotNull',
 'JDPowersCat_numReturnedasm1',
 'JDPowersCat_numTransactions',
 'LIGHTG',
 'LIGHTR',
 'LIGHTY',
 'LIGHT_N0G1Y2R3',
 'MMR',
 'Mileage',
 'OVE',
 'PSI',
 'PSIEligible',
 'Returned_asm',
 'SaleDate_day',
 'SaleDate_decyear',
 'SaleDate_dow',
 'SaleDate_doy',
 'SalePrice',
 'Salvage',
 'SellerID_fracDSEligible1DivTransactions',
 'SellerID_fracReturned1DivReturnedNotNull',
 'SellerID_fracReturnedNotNullDivDSEligible1',
 'SellerID_fracReturnedasm1DivTransactions',
 'SellerID_numDSEligible1',
 'SellerID_numReturned1',
 'SellerID_numReturnedNotNull',
 'SellerID_numReturnedasm1',
 'SellerID_numTransactions',
 'SellingLocation_fracDSEligible1DivTransactions',
 'SellingLocation_fracReturned1DivReturnedNotNull',
 'SellingLocation_fracReturnedNotNullDivDSEligible1',
 'SellingLocation_fracReturnedasm1DivTransactions',
 'SellingLocation_lat',
 'SellingLocation_lon',
 'SellingLocation_numDSEligible1',
 'SellingLocation_numReturned1',
 'SellingLocation_numReturnedNotNull',
 'SellingLocation_numReturnedasm1',
 'SellingLocation_numTransactions',
 'Simulcast',
 'VIN_fracDSEligible1DivTransactions',
 'VIN_fracReturned1DivReturnedNotNull',
 'VIN_fracReturnedNotNullDivDSEligible1',
 'VIN_fracReturnedasm1DivTransactions',
 'VIN_numDSEligible1',
 'VIN_numReturned1',
 'VIN_numReturnedNotNull',
 'VIN_numReturnedasm1',
 'VIN_numTransactions']

print(textwrap.dedent("""\
    `Container`: Create an empty container class and
    dynamically allocate attributes to hold variables for specific steps
    of the pipeline. """))
Container = demo.utils.utils.Container
step = Container()

print(textwrap.dedent("""\
    `step.s0.[df,ds]_[features,target]`: Save initial state of features, target."""))
step.s0 = Container()
step.s0.dfs = Container()
step.s0.dfs.df_features = df[features].copy()
step.s0.dfs.ds_target = df[target].copy()

# TODO: REDO after this point with step.sN.dfs.[df_features,ds_target]
# rather than redefining [df_features,ds_target]
df_features = step.s0.dfs.df_features
ds_target = step.s0.dfs.ds_target

`Container`: Create an empty container class and
dynamically allocate attributes to hold variables for specific steps
of the pipeline. 
`step.s0.[df,ds]_[features,target]`: Save initial state of features, target.

print(textwrap.dedent("""\
    `transformer_scaler`, `transformer_pca`: Scale data
    then make groups of similar records with k-means clustering,
    both with and without PCA. Use the silhouette score to determine
    the number of clusters.
    """))
time_start = time.perf_counter()

# Scale data prior to comparing clusters with/without PCA. 
# Note: Using sklearn.preprocessing.RobustScaler with
#     sklearn.decomposition.IncrementalPCA(whiten=False)
#     is often the most stable (slowly varying scores)
#     with highest scores. Centroid agreement can still be
#     off due to outliers.
transformer_scaler = sk_pre.RobustScaler()
features_scaled = transformer_scaler.fit_transform(X=df_features)
transformer_pca = sk_dc.IncrementalPCA(whiten=False)
features_scaled_pca = transformer_pca.fit_transform(X=features_scaled)

print("`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.")
path_data = path_data_dir
path_cols = os.path.join(path_data, 'columns.pkl')
with open(path_cols, mode='wb') as fobj:
    pickle.dump(obj=df_features.columns, file=fobj)
path_tform_scl = os.path.join(path_data, 'transformer_scaler.pkl')
with open(path_tform_scl, mode='wb') as fobj:
    pickle.dump(obj=transformer_scaler, file=fobj)
path_tform_pca = os.path.join(path_data, 'transformer_pca.pkl')
with open(path_tform_pca, mode='wb') as fobj:
    pickle.dump(obj=transformer_pca, file=fobj)

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    
    print("Plot scores for scaled features:")
    demo.utils.utils.calc_silhouette_scores(
        df_features=features_scaled, n_clusters_min=2, n_clusters_max=10,
        size_sub=None, n_scores=10, show_progress=True, show_plot=True)

    print("Plot scores for scaled PCA features:")
    demo.utils.utils.calc_silhouette_scores(
        df_features=features_scaled_pca, n_clusters_min=2, n_clusters_max=10,
        size_sub=None, n_scores=10, show_progress=True, show_plot=True)

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

`transformer_scaler`, `transformer_pca`: Scale data
then make groups of similar records with k-means clustering,
both with and without PCA. Use the silhouette score to determine
the number of clusters.

`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
Plot scores for scaled features:
Progress: 11% 22% 33% 44% 56% 67% 78% 89% 100%

Plot scores for scaled PCA features:
Progress: 11% 22% 33% 44% 56% 67% 78% 89% 100%

Time elapsed (sec) = 47.4

print(textwrap.dedent("""\
    `transformer_kmeans`, `transformer_kmeans_pca`:
    Fit k-means to the data with/without PCA and
    compare the centroids for the clusters."""))

# TODO: Fix plot. Assign clusters IDs in a deterministic way so that
#   cluster 0 raw matches cluster 0 transformed.
time_start = time.perf_counter()

n_clusters = 2 # from silhouette scores

with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    # Cluster scaled features with/without PCA using minibatch k-means
    transformer_kmeans = sk_cl.MiniBatchKMeans(n_clusters=n_clusters)
    transformer_kmeans.fit(X=features_scaled)
    transformer_kmeans_pca = sk_cl.MiniBatchKMeans(n_clusters=n_clusters)
    transformer_kmeans_pca.fit(X=features_scaled_pca)

print("`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.")
path_tform_km = os.path.join(path_data, 'transformer_kmeans.pkl')
with open(path_tform_km, mode='wb') as fobj:
    pickle.dump(obj=transformer_kmeans, file=fobj)
path_tform_km_pca = os.path.join(path_data, 'transformer_kmeans_pca.pkl')
with open(path_tform_km_pca, mode='wb') as fobj:
    pickle.dump(obj=transformer_kmeans_pca, file=fobj)

with warnings.catch_warnings():
    warnings.simplefilter("ignore")

    # Plot clusters in scaled feature space.
    centroids = transformer_kmeans.cluster_centers_
    transformed_centroids = transformer_pca.inverse_transform(transformer_kmeans_pca.cluster_centers_)
    (col_1, col_0) = np.argsort(np.var(features_scaled, axis=0))[-2:]
    (name_1, name_0) = (df_features.columns.values[col_1], df_features.columns.values[col_0])
    plt.title("Data and centroids within scaled feature space")
    tfmask_gt01 = df_features[buyer_retrate] > buyer_retrate_max
    plt.plot(features_scaled[tfmask_gt01, col_0], features_scaled[tfmask_gt01, col_1],
             marker='o', linestyle='', color=sns.color_palette()[2], alpha=0.5,
             label='data, buyer_retrate_gt01')
    tfmask_lt01 = np.logical_not(tfmask_gt01)
    plt.plot(features_scaled[tfmask_lt01, col_0], features_scaled[tfmask_lt01, col_1],
             marker='.', linestyle='', color=sns.color_palette()[1], alpha=0.5,
             label='data, buyer_retrate_lt01')
    plt.plot(centroids[:, col_0], centroids[:, col_1],
             marker='+', linestyle='', markeredgewidth=2, markersize=12,
             color=sns.color_palette()[0], label='centroids')
    for (idx, centroid) in enumerate(centroids):
        plt.annotate(
            str(idx), xy=(centroid[col_0], centroid[col_1]),
            xycoords='data', xytext=(0, 0), textcoords='offset points', color='black',
            fontsize=18, rotation=0)
    plt.plot(transformed_centroids[:, col_0], transformed_centroids[:, col_1],
             marker='x', linestyle='', markeredgewidth=2, markersize=10,
             color=sns.color_palette()[1], label='transformed centroids')
    for (idx, transformed_centroid) in enumerate(transformed_centroids):
        plt.annotate(
            str(idx), xy=(transformed_centroid[col_0], transformed_centroid[col_1]),
            xycoords='data', xytext=(0, 0), textcoords='offset points', color='black',
            fontsize=18, rotation=0)
    plt.xlabel("Scaled '{name}', highest variance".format(name=name_0))
    plt.ylabel("Scaled '{name}', next highest variance".format(name=name_1))
    plt.legend(loc='upper left')
    plt.show()

with warnings.catch_warnings():
    warnings.simplefilter("ignore")

    # Plot clusters in scaled feature PCA space.
    transformed_centroids = transformer_pca.transform(transformer_kmeans.cluster_centers_)
    centroids = transformer_kmeans_pca.cluster_centers_
    plt.title("Data and centroids within scaled feature PCA space")
    plt.plot(features_scaled_pca[tfmask_gt01, 0], features_scaled_pca[tfmask_gt01, 1],
             marker='o', linestyle='', color=sns.color_palette()[2], alpha=0.5,
             label='transformed data, buyer_retrate_gt01')
    plt.plot(features_scaled_pca[tfmask_lt01, 0], features_scaled_pca[tfmask_lt01, 1],
             marker='.', linestyle='', color=sns.color_palette()[1], alpha=0.5,
             label='transformed data, buyer_retrate_lt01')
    plt.plot(transformed_centroids[:, 0], transformed_centroids[:, 1],
             marker='+', linestyle='', markeredgewidth=2, markersize=12,
             color=sns.color_palette()[0], label='transformed centroids')
    for (idx, transformed_centroid) in enumerate(transformed_centroids):
        plt.annotate(
            str(idx), xy=(transformed_centroid[0], transformed_centroid[1]),
            xycoords='data', xytext=(0, 0), textcoords='offset points', color='black',
            fontsize=18, rotation=0)
    plt.plot(centroids[:, 0], centroids[:, 1],
             marker='x', linestyle='', markeredgewidth=2, markersize=10,
             color=sns.color_palette()[1], label='centroids')
    for (idx, centroid) in enumerate(centroids):
        plt.annotate(
            str(idx), xy=(centroid[0], centroid[1]),
            xycoords='data', xytext=(0, 0), textcoords='offset points', color='black',
            fontsize=18, rotation=0)
    plt.xlabel('Principal component 0')
    plt.ylabel('Principal component 1')
    plt.legend(loc='upper left')
    plt.show()

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

`transformer_kmeans`, `transformer_kmeans_pca`:
Fit k-means to the data with/without PCA and
compare the centroids for the clusters.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.

Time elapsed (sec) = 3.0

print(textwrap.dedent("""\
    `df_features2`: Combine `df_features` with
    cluster labels, cluster distances, PCA components, PCA cluster labels,
    and PCA cluster distances into `df_features`."""))
time_start = time.perf_counter()

# Cluster labels and distances in feature space.
ds_clusters = pd.Series(
    transformer_kmeans.predict(X=features_scaled),
    index=df_features.index, name='cluster')
n_digits = len(str(len(transformer_kmeans.cluster_centers_)))
columns = [
    'cluster_{num}_dist'.format(num=str(num).rjust(n_digits, '0'))
    for num in range(len(transformer_kmeans.cluster_centers_))]
df_cluster_dists = pd.DataFrame(
    transformer_kmeans.transform(X=features_scaled),
    index=df_features.index, columns=columns)
if not np.all(ds_clusters.values == np.argmin(df_cluster_dists.values, axis=1)):
    raise AssertionError(
        ("Program error. Not all cluster labels match cluster label\n" +
         "with minimum distance to record.\n" +
         "Required: np.all(ds_clusters.values == np.argmin(df_cluster_dists.values, axis=1))"))

# PCA features.
n_digits = len(str(transformer_pca.n_components_))
columns = [
    'pca_comp_{num}'.format(num=str(num).rjust(n_digits, '0'))
    for num in range(transformer_pca.n_components_)]
df_features_pca = pd.DataFrame(
    features_scaled_pca, index=df_features.index, columns=columns)

# Cluster labels and distances in PCA feature space.
ds_clusters_pca = pd.Series(
    transformer_kmeans_pca.predict(X=features_scaled_pca),
    index=df_features.index, name='pca_cluster')
n_digits = len(str(len(transformer_kmeans_pca.cluster_centers_)))
columns = [
    'pca_cluster_{num}_dist'.format(num=str(num).rjust(n_digits, '0'))
    for num in range(len(transformer_kmeans_pca.cluster_centers_))]
df_cluster_dists_pca = pd.DataFrame(
    transformer_kmeans_pca.transform(X=features_scaled_pca),
    index=df_features.index, columns=columns)
if not np.all(ds_clusters_pca.values == np.argmin(df_cluster_dists_pca.values, axis=1)):
    raise AssertionError(
        ("Program error. Not all PCA cluster labels match PCA cluster label\n" +
         "with minimum distance to record.\n" +
         "Required: np.all(ds_clusters_pca.values == np.argmin(df_cluster_dists_pca.values, axis=1))"))

# Combine with original `df_features` into new `df_features2`.
df_features2 = pd.concat(
    [df_features, ds_clusters, df_cluster_dists,
     df_features_pca, ds_clusters_pca, df_cluster_dists_pca],
    axis=1, copy=True)

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

`df_features2`: Combine `df_features` with
cluster labels, cluster distances, PCA components, PCA cluster labels,
and PCA cluster distances into `df_features`.
Time elapsed (sec) = 1.3

print(textwrap.dedent("""\
    `df_importances` , `important_features`, `df_features3`:
    `df_features3` is a view into (not a copy) of `df_features2` with only
    `important_features`. Feature importance is the normalized reduction
    in the loss score. A feature is selected as 'important' if its average
    importance is greater than the average importance of the random feature."""))
time_start = time.perf_counter()

# Calculate feature importances.
# Note:
# * `n_estimators` impact the feature importances but only have a small
#     effect on the relative importances.
# * `n_estimators` impact the scores but only have a small effect on the relative scores.
# * Use replace=False for maximum data variety.
# TODO: Use a significance test for feature importance.
estimator = sk_ens.ExtraTreesRegressor(n_estimators=10, n_jobs=-1)
df_importances = demo.utils.utils.calc_feature_importances(
    estimator=estimator, df_features=df_features2, ds_target=ds_target,
    replace=False, show_progress=True, show_plot=True)
important_features = df_importances.columns[
    df_importances.mean() > df_importances['random'].mean()]
important_features = list(
    df_importances[important_features].mean().sort_values(ascending=False).index)
df_features3 = df_features2[important_features]
print("`important_features` =")
print(important_features)
print()

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

`df_importances` , `important_features`, `df_features3`:
`df_features3` is a view into (not a copy) of `df_features2` with only
`important_features`. Feature importance is the normalized reduction
in the loss score. A feature is selected as 'important' if its average
importance is greater than the average importance of the random feature.
Progress: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_78', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_72', 'pca_comp_76', 'pca_comp_75', 'pca_comp_73', 'DSEligible', 'VIN_fracDSEligible1DivTransactions', 'VIN_numReturnedasm1', 'VIN_numReturned1', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_03', 'pca_comp_57', 'pca_comp_01', 'pca_comp_31', 'pca_comp_49', 'pca_comp_50', 'BuyerID_numReturnedNotNull', 'pca_comp_12', 'pca_comp_02', 'VIN_numTransactions', 'pca_comp_68', 'BuyerID_fracDSEligible1DivTransactions', 'SellerID_fracReturnedNotNullDivDSEligible1', 'Mileage', 'pca_comp_54', 'pca_cluster', 'VIN_numDSEligible1', 'BuyerID_numReturned1', 'pca_comp_06', 'pca_comp_38', 'pca_comp_47', 'pca_comp_09', 'pca_comp_28', 'pca_comp_04', 'pca_comp_25', 'pca_comp_61', 'SalePrice', 'BuyerID_numTransactions', 'SellingLocation_numReturnedasm1', 'SellingLocation_numReturnedNotNull', 'CarMake_fracReturnedasm1DivTransactions', 'BuyerID_fracReturned1DivReturnedNotNull', 'SellerID_numDSEligible1', 'cluster_0_dist', 'pca_comp_41', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_48', 'JDPowersCat_numReturned1', 'CarMake_numReturned1', 'pca_comp_08', 'pca_comp_66', 'pca_comp_43', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_37']

Time elapsed (sec) = 9.9

print("`df_features`: Most significant projections of PCA component 78:")
print(sorted(list(zip(df_features, transformer_pca.components_[78])), key=lambda tup: tup[1])[:3])
print('...')
print(sorted(list(zip(df_features, transformer_pca.components_[78])), key=lambda tup: tup[1])[-3:])

`df_features`: Most significant projections of PCA component 78:
[('DSEligible', -0.39888862828646721), ('VIN_numReturned1', -0.16018987870664869), ('Returned_asm', -0.15564636661469736)]
...
[('VIN_numTransactions', 0.094004552775801192), ('VIN_fracReturnedasm1DivTransactions', 0.39932190389544714), ('VIN_fracDSEligible1DivTransactions', 0.76826145356509656)]

print(textwrap.dedent("""\
    Tune feature space by optimizing the model score
    with cross validation. Model scores are R^2,
    the coefficient of determination."""))
time_start = time.perf_counter()

print("Progress:", end=' ')
size_data = len(df_features3)
size_sub = 1000
frac_test = 0.2
replace = False
n_scores = 10
estimator = sk_ens.ExtraTreesRegressor(n_estimators=10, n_jobs=-1)
nftrs_scores = list()
idxs = itertools.chain(range(0, 20), range(20, 50, 2), range(50, len(important_features), 5))
for idx in idxs:
    n_ftrs = idx+1
    ftrs = important_features[:n_ftrs]
    scores = list()
    for _ in range(0, n_scores):
        idxs_sub = np.random.choice(a=size_data, size=size_sub, replace=replace)
        (ftrs_train, ftrs_test,
         trg_train, trg_test) = sk_cv.train_test_split(
            df_features3[ftrs].values[idxs_sub], ds_target.values[idxs_sub],
            test_size=frac_test)
        estimator.fit(X=ftrs_train, y=trg_train)
        scores.append(estimator.score(X=ftrs_test, y=trg_test))
    nftrs_scores.append([n_ftrs, scores])
    if idx % 10 == 0:
        print("{frac:.0%}".format(frac=(idx+1)/len(important_features)), end=' ')
print('\n')

nftrs_pctls = np.asarray(
    [np.append(tup[0], np.percentile(tup[1], q=[5,50,95]))
     for tup in nftrs_scores])
plt.plot(
    nftrs_pctls[:, 0], nftrs_pctls[:, 2],
    marker='.', color=sns.color_palette()[0],
    label='50th pctl score')
plt.fill_between(
    nftrs_pctls[:, 0],
    y1=nftrs_pctls[:, 1],
    y2=nftrs_pctls[:, 3],
    alpha=0.5, color=sns.color_palette()[0],
    label='5-95th pctls of scores')
plt.title("Model score vs number of features")
plt.xlabel("Number of features")
plt.ylabel("Model score")
plt.legend(loc='upper left')
plt.show()

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

Tune feature space by optimizing the model score
with cross validation. Model scores are R^2,
the coefficient of determination.
Progress: 2% 19% 36% 53% 69% 86%

Time elapsed (sec) = 95.5

print("""`important_features2`, `df_features4`:
`df_features4` is a view into (not a copy) of `df_features3` with only
`important_features2`. Feature importance is the normalized reduction
in the loss score. A feature is selected as 'important' from the
model score vs features plot.
""")
time_start = time.perf_counter()

# Keep top 10 features from score vs features plot.
important_features2 = important_features[:10]

df_features4 = df_features3[important_features2]
print("`important_features2` =")
print(important_features2)
print()

print("""Cluster map of important feature correlations with heirarchical relationships.
The deeper of the dendrogram node, the higher (anti)correlated the features are.
The Spearman rank correlation accommodates non-linear features.
The pair plot is a scatter matrix plot of columns vs each other.
""")
path_plot_dir = os.path.join(path_data_dir, 'plot_model')

# Notes:
# * `size_sub` for computing correlations should be <= 1e3 else runtime is long.
# * Use replace=False to show most data variety.
# * For pairplot, only plot the target variable with the top 5 important
#     features for legibility.
# * For clustermap, `nlabels` shows every `nlabels`th label, so 20 labels total.
size_sub = min(int(1e3), len(df_features4.index))
idxs_sub = np.random.choice(a=df_features4.index, size=size_sub, replace=False)
df_plot_sub = df_features4.loc[idxs_sub].copy()
df_plot_sub[target] = ds_target.loc[idxs_sub].copy()
df_plot_sub['buyer_retrate_gt01'] = df_features3.loc[idxs_sub, buyer_retrate] > buyer_retrate_max

print(("Clustermap of target, '{target}', top 10 important features, buyer_retrate_gt01:").format(
        target=target))
sns.clustermap(df_plot_sub[[target]+important_features2[:10]+['buyer_retrate_gt01']].corr(method='spearman'))
plt.savefig(
    os.path.join(path_plot_dir, 'model_clustermap.png'),
    bbox_inches='tight', dpi=300)
plt.show()

print(("Pairplot of target, '{target}', top 5 important features, buyer_retrate_gt01:").format(
        target=target))
df_pairplot = df_plot_sub[[target]+important_features2[:5]+['buyer_retrate_gt01']]
print(df_pairplot.columns)
ds_columns = pd.Series(df_pairplot.columns, name='column')
ds_columns.to_csv(
    os.path.join(path_plot_dir, 'model_pairplot_index_column_map.csv'),
    header=True, index_label='index')
df_pairplot.columns = ds_columns.index
df_pairplot.loc[:, target] = df_pairplot[np.where(ds_columns.values == target)[0][0]]
df_pairplot.loc[:, 'buyer_retrate_gt01'] = df_pairplot[np.where(ds_columns.values == 'buyer_retrate_gt01')[0][0]]
df_pairplot.drop([np.where(ds_columns.values == target)[0][0]], axis=1, inplace=True)
df_pairplot.drop([np.where(ds_columns.values == 'buyer_retrate_gt01')[0][0]], axis=1, inplace=True)
sns.pairplot(
    df_pairplot,
    hue='buyer_retrate_gt01', diag_kind='hist', markers=['.', 'o'],
    palette=[sns.color_palette()[1], sns.color_palette()[2]],
    plot_kws={'alpha':1.0})
plt.savefig(
    os.path.join(path_plot_dir, 'model_pairplot.png'),
    bbox_inches='tight', dpi=300)
plt.show()

print("Summarize top 5 important features:")
print(df_features4[important_features2[:5]].describe(percentiles=percentiles, include='all'))
print()
print("First 5 records for top 5 important features:")
print(df_features4[important_features2[:5]].head())
print()
print("""Describe top 5 important features. Format:
Feature: importance score.
Histogram of feature values.""")
cols_scores = df_importances[important_features2[:5]].mean().items()
for (col, score) in cols_scores:
    # Describe feature variables.
    print(
        ("{col}:\n" +
         "    importance: {score:.3f}").format(col=col, score=score))
    # Plot histogram of feature variables.
    tfmask_gt01 = df_features3[buyer_retrate] > buyer_retrate_max
    sns.distplot(
        df_features4.loc[np.logical_not(tfmask_gt01), col], hist=True, kde=False, norm_hist=False,
        label='buyer_retrate_lt01', color=sns.color_palette()[1])
    sns.distplot(
        df_features4.loc[tfmask_gt01, col], hist=True, kde=False, norm_hist=False,
        label='buyer_retrate_gt01', color=sns.color_palette()[2])
    plt.title('Feature value histogram')
    plt.xlabel("Feature value, '{ftr}'".format(ftr=col))
    plt.ylabel('Number of feature values')
    plt.legend(loc='upper left')
    plt.show()

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

`important_features2`, `df_features4`:
`df_features4` is a view into (not a copy) of `df_features3` with only
`important_features2`. Feature importance is the normalized reduction
in the loss score. A feature is selected as 'important' from the
model score vs features plot.

`important_features2` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_78', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_72', 'pca_comp_76', 'pca_comp_75', 'pca_comp_73']

Cluster map of important feature correlations with heirarchical relationships.
The deeper of the dendrogram node, the higher (anti)correlated the features are.
The Spearman rank correlation accommodates non-linear features.
The pair plot is a scatter matrix plot of columns vs each other.

Clustermap of target, 'Returned', top 10 important features, buyer_retrate_gt01:

Pairplot of target, 'Returned', top 5 important features, buyer_retrate_gt01:
Index(['Returned', 'VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm',
       'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull',
       'pca_comp_78', 'buyer_retrate_gt01'],
      dtype='object')

/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py:288: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[key] = _infer_fill_value(value)
/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py:465: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[item] = s
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:55: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/opt/conda/lib/python3.5/site-packages/ipykernel/__main__.py:56: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1   Returned_asm  \
count                          278337.000000  278337.000000   
mean                                0.072437       0.070095   
std                                 0.254448       0.255307   
min                                 0.000000       0.000000   
5%                                  0.000000       0.000000   
15.9%                               0.000000       0.000000   
50%                                 0.000000       0.000000   
84.1%                               0.000000       0.000000   
95%                                 1.000000       1.000000   
max                                 1.000000       1.000000   

       VIN_numReturnedNotNull  VIN_fracReturned1DivReturnedNotNull  \
count           278337.000000                        278337.000000   
mean                 0.079350                             0.020815   
std                  0.277162                             0.141990   
min                  0.000000                             0.000000   
5%                   0.000000                             0.000000   
15.9%                0.000000                             0.000000   
50%                  0.000000                             0.000000   
84.1%                0.000000                             0.000000   
95%                  1.000000                             0.000000   
max                  4.000000                             1.000000   

        pca_comp_78  
count  2.783370e+05  
mean   2.431927e-17  
std    8.707895e-03  
min   -2.556930e-01  
5%    -3.498293e-03  
15.9% -5.717638e-04  
50%    3.778332e-05  
84.1%  5.294729e-04  
95%    1.209417e-03  
max    2.741183e-01  

First 5 records for top 5 important features:
   VIN_fracReturnedNotNullDivDSEligible1  Returned_asm  \
0                                    0.0             0   
1                                    0.0             0   
2                                    0.0             0   
3                                    0.0             0   
4                                    0.0             0   

   VIN_numReturnedNotNull  VIN_fracReturned1DivReturnedNotNull  pca_comp_78  
0                       0                                  0.0     0.000569  
1                       0                                  0.0     0.000502  
2                       0                                  0.0     0.001204  
3                       0                                  0.0     0.000335  
4                       0                                  0.0     0.001024  

Describe top 5 important features. Format:
Feature: importance score.
Histogram of feature values.
VIN_fracReturnedNotNullDivDSEligible1:
    importance: 0.758

Returned_asm:
    importance: 0.109

VIN_numReturnedNotNull:
    importance: 0.075

VIN_fracReturned1DivReturnedNotNull:
    importance: 0.016

pca_comp_78:
    importance: 0.009

Time elapsed (sec) = 22.7

print("""Tune model hyperparameters by optimizing the model score
with cross validation. Model scores are R^2,
the coefficient of determination.
""")
time_start = time.perf_counter()

print("Progress:", end=' ')
size_data = len(df_features4)
size_sub = min(len(df_features4), int(2e3))
frac_test = 0.2
replace = False
nest_list = [10, 30, 100, 300]
n_scores = 10
nest_scores = list()
for (inum, n_est) in enumerate(nest_list):
    estimator = sk_ens.ExtraTreesRegressor(n_estimators=n_est, n_jobs=-1)
    scores = list()
    for _ in range(0, n_scores):
        idxs_sub = np.random.choice(a=size_data, size=size_sub, replace=replace)
        (ftrs_train, ftrs_test,
         trg_train, trg_test) = sk_cv.train_test_split(
            df_features4.values[idxs_sub], ds_target.values[idxs_sub],
            test_size=frac_test)
        estimator.fit(X=ftrs_train, y=trg_train)
        scores.append(estimator.score(
                X=ftrs_test, y=trg_test))
    nest_scores.append([n_est, scores])
    print("{frac:.0%}".format(frac=(inum+1)/len(nest_list)), end=' ')
print('\n')

nest_pctls = np.asarray(
    [np.append(tup[0], np.percentile(tup[1], q=[5,50,95]))
     for tup in nest_scores])
plt.plot(
    nest_pctls[:, 0], nest_pctls[:, 2],
    marker='.', color=sns.color_palette()[0],
    label='50th pctl score')
plt.fill_between(
    nest_pctls[:, 0],
    y1=nest_pctls[:, 1],
    y2=nest_pctls[:, 3],
    alpha=0.5, color=sns.color_palette()[0],
    label='5-95th pctls of scores')
plt.title("Model score vs number of estimators")
plt.xlabel("Number of estimators")
plt.ylabel("Model score")
plt.legend(loc='lower left')
plt.show()

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

Tune model hyperparameters by optimizing the model score
with cross validation. Model scores are R^2,
the coefficient of determination.

Progress: 25% 50% 75% 100%

Time elapsed (sec) = 11.4

print("""Test significance of predictions by shuffling the target values.
Model scores are r^2, the coefficient of determination.
""")
n_estimators = 50 # from tuning curve
time_start = time.perf_counter()

# Calculate significance of score.
estimator = sk_ens.ExtraTreesRegressor(n_estimators=n_estimators, n_jobs=-1)
demo.utils.utils.calc_score_pvalue(
    estimator=estimator, df_features=df_features4, ds_target=ds_target,
    n_iter=20, size_sub=None, frac_test=0.2,
    replace=False, show_progress=True, show_plot=True)
print()

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

Test significance of predictions by shuffling the target values.
Model scores are r^2, the coefficient of determination.

Progress: 5% 25% 45% 65% 85%

/opt/conda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

Average model score with shuffling: -0.116
Average model score without shuffling: 0.991

Null hypothesis: There is no distinction in the differences
between the mean model scores whether or not the target
values have been shuffled.
Outcome: Assuming the null hypothesis, the probability of
obtaining a difference between the mean model scores at least
as great as 1.11 is 0.0%.

Time elapsed (sec) = 11.8

print("""Predict target values with cross-validation,
plot actual vs predicted and score.
""")
n_estimators = 50 # from tuning curve
time_start = time.perf_counter()

print("Progress:", end=' ')
n_folds = 5
estimator = sk_ens.ExtraTreesRegressor(n_estimators=n_estimators, n_jobs=-1)
kfolds = sk_cv.KFold(n=len(df_features4), n_folds=n_folds, shuffle=True)
ds_predicted = pd.Series(index=ds_target.index, name=target+'_pred')
idxs_pred = set()
for (inum, (idxs_train, idxs_test)) in enumerate(kfolds):
    if not idxs_pred.isdisjoint(idxs_test):
        raise AssertionError(
            ("Program error. Each record must be predicted only once.\n" +
             "Required: idxs_pred.isdisjoint(idxs_test)"))
    idxs_pred.update(idxs_test)
    ftrs_train = df_features4.values[idxs_train]
    ftrs_test  = df_features4.values[idxs_test]
    trg_train  = ds_target.values[idxs_train]
    trg_test   = ds_target.values[idxs_test]
    estimator.fit(X=ftrs_train, y=trg_train)
    ds_predicted.iloc[idxs_test] = estimator.predict(X=ftrs_test)
    print("{frac:.0%}".format(frac=(inum+1)/n_folds), end=' ')
print('\n')

score = sk_met.r2_score(
    y_true=ds_target, y_pred=ds_predicted)
print("Model score = {score:.3f}".format(score=score))
path_plot_dir = os.path.join(path_data_dir, 'plot_model')
demo.utils.utils.plot_actual_vs_predicted(
    y_true=ds_target.values, y_pred=ds_predicted.values,
    loglog=False, xylims=(-1.1, 1.1),
    path=os.path.join(path_plot_dir, 'model_actual_vs_predicted.jpg'))

print("""`features.pkl`, `estimator.pkl`: Save features and estimator.
""")
path_ftr = os.path.join(path_data, 'features.pkl')
with open(path_ftr, mode='wb') as fobj:
    pickle.dump(obj=df_features4.columns, file=fobj)
path_est = os.path.join(path_data, 'estimator.pkl')
with open(path_est, mode='wb') as fobj:
    pickle.dump(obj=estimator, file=fobj)

time_stop = time.perf_counter()
print("Time elapsed (sec) = {diff:.1f}".format(diff=time_stop-time_start))

Predict target values with cross-validation,
plot actual vs predicted and score.

Progress: 20% 40% 60% 80% 100% 

Model score = 0.995

`features.pkl`, `estimator.pkl`: Save features and estimator.

Time elapsed (sec) = 7.4

demo.app_predict.predict.create_pipeline_model(df=df, path_data_dir=path_data_dir, show_plots=False)

################################################################################
Features:
['Arbitrated', 'Autocheck_score', 'BuyerID_fracDSEligible1DivTransactions', 'BuyerID_fracReturned1DivReturnedNotNull', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_fracReturnedasm1DivTransactions', 'BuyerID_numDSEligible1', 'BuyerID_numReturned1', 'BuyerID_numReturnedNotNull', 'BuyerID_numReturnedasm1', 'BuyerID_numTransactions', 'CarMake_fracDSEligible1DivTransactions', 'CarMake_fracReturned1DivReturnedNotNull', 'CarMake_fracReturnedNotNullDivDSEligible1', 'CarMake_fracReturnedasm1DivTransactions', 'CarMake_numDSEligible1', 'CarMake_numReturned1', 'CarMake_numReturnedNotNull', 'CarMake_numReturnedasm1', 'CarMake_numTransactions', 'CarYear', 'ConditionReport', 'DSEligible', 'InLane', 'JDPowersCat_COMPACTCAR', 'JDPowersCat_EXCLUDED', 'JDPowersCat_FULLSIZECAR', 'JDPowersCat_LUXURYCAR', 'JDPowersCat_MIDSIZECAR', 'JDPowersCat_PICKUP', 'JDPowersCat_SPORTSCAR', 'JDPowersCat_SUV', 'JDPowersCat_UNKNOWN', 'JDPowersCat_VAN', 'JDPowersCat_fracDSEligible1DivTransactions', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'JDPowersCat_fracReturnedasm1DivTransactions', 'JDPowersCat_numDSEligible1', 'JDPowersCat_numReturned1', 'JDPowersCat_numReturnedNotNull', 'JDPowersCat_numReturnedasm1', 'JDPowersCat_numTransactions', 'LIGHTG', 'LIGHTR', 'LIGHTY', 'LIGHT_N0G1Y2R3', 'MMR', 'Mileage', 'OVE', 'PSI', 'PSIEligible', 'Returned_asm', 'SaleDate_day', 'SaleDate_decyear', 'SaleDate_dow', 'SaleDate_doy', 'SalePrice', 'Salvage', 'SellerID_fracDSEligible1DivTransactions', 'SellerID_fracReturned1DivReturnedNotNull', 'SellerID_fracReturnedNotNullDivDSEligible1', 'SellerID_fracReturnedasm1DivTransactions', 'SellerID_numDSEligible1', 'SellerID_numReturned1', 'SellerID_numReturnedNotNull', 'SellerID_numReturnedasm1', 'SellerID_numTransactions', 'SellingLocation_fracDSEligible1DivTransactions', 'SellingLocation_fracReturned1DivReturnedNotNull', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellingLocation_fracReturnedasm1DivTransactions', 'SellingLocation_lat', 'SellingLocation_lon', 'SellingLocation_numDSEligible1', 'SellingLocation_numReturned1', 'SellingLocation_numReturnedNotNull', 'SellingLocation_numReturnedasm1', 'SellingLocation_numTransactions', 'Simulcast', 'VIN_fracDSEligible1DivTransactions', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'VIN_fracReturnedasm1DivTransactions', 'VIN_numDSEligible1', 'VIN_numReturned1', 'VIN_numReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_numTransactions']

################################################################################
`Container`: Create an empty container class and
dynamically allocate attributes to hold variables for specific steps
of the pipeline. 
`step.s0.[df,ds]_[features,target]`: Save initial state of features, target.

################################################################################
`transformer_scaler`, `transformer_pca`: Scale data
then make groups of similar records with k-means clustering,
both with and without PCA. Use the silhouette score to determine
the number of clusters.

`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
Plot scores for scaled features:
Progress: 11% 22% 33% 44% 56% 67% 78% 89% 100% 

Plot scores for scaled PCA features:
Progress: 11% 22% 33% 44% 56% 67% 78% 89% 100% 

Time elapsed (sec) = 51.5

################################################################################
`transformer_kmeans`, `transformer_kmeans_pca`:
Fit k-means to the data with/without PCA and
compare the centroids for the clusters.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
Time elapsed (sec) = 1.5

################################################################################
`df_features2`: Combine `df_features` with
cluster labels, cluster distances, PCA components, PCA cluster labels,
and PCA cluster distances into `df_features`.
Time elapsed (sec) = 1.5

################################################################################
`df_importances` , `important_features`, `df_features3`:
`df_features3` is a view into (not a copy) of `df_features2` with only
`important_features`. Feature importance is the normalized reduction
in the loss score. A feature is selected as 'important' if its average
importance is greater than the average importance of the random feature.
Progress: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 

`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_78', 'VIN_fracReturnedasm1DivTransactions', 'DSEligible', 'pca_comp_75', 'pca_comp_73', 'pca_comp_76', 'pca_comp_72', 'VIN_numReturned1', 'VIN_fracDSEligible1DivTransactions', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedasm1', 'pca_comp_68', 'VIN_numDSEligible1', 'pca_comp_03', 'pca_comp_49', 'pca_comp_57', 'pca_comp_02', 'LIGHTY', 'pca_comp_01', 'Arbitrated', 'pca_cluster_0_dist', 'BuyerID_numReturnedNotNull', 'CarMake_fracReturned1DivReturnedNotNull', 'SalePrice', 'Mileage', 'pca_comp_36', 'pca_comp_50', 'pca_comp_33', 'pca_comp_38', 'pca_comp_06', 'MMR', 'pca_comp_40', 'pca_comp_10', 'VIN_numTransactions', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_12', 'pca_comp_17', 'JDPowersCat_numDSEligible1', 'cluster_0_dist', 'pca_cluster', 'pca_comp_77', 'pca_comp_31', 'BuyerID_fracDSEligible1DivTransactions', 'SellerID_fracReturnedasm1DivTransactions', 'pca_comp_65', 'pca_comp_04', 'pca_comp_32', 'pca_comp_11', 'pca_comp_44', 'CarYear', 'SellingLocation_lat', 'pca_comp_62', 'pca_comp_18', 'JDPowersCat_numReturnedNotNull', 'pca_comp_48', 'BuyerID_numDSEligible1', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_30', 'pca_comp_63', 'pca_comp_00', 'pca_comp_47', 'SellingLocation_lon', 'pca_comp_64', 'pca_cluster_1_dist', 'cluster', 'pca_comp_51', 'BuyerID_numReturnedasm1', 'pca_comp_54', 'pca_comp_53', 'pca_comp_67', 'pca_comp_69', 'Autocheck_score', 'pca_comp_19', 'pca_comp_66', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'pca_comp_22', 'pca_comp_70', 'pca_comp_28', 'pca_comp_56']

Time elapsed (sec) = 11.9
`df_features`: Most significant projections of PCA component 78:
[('DSEligible', -0.39888862828646721), ('VIN_numReturned1', -0.16018987870664869), ('Returned_asm', -0.15564636661469736)]
...
[('VIN_numTransactions', 0.094004552775801192), ('VIN_fracReturnedasm1DivTransactions', 0.39932190389544714), ('VIN_fracDSEligible1DivTransactions', 0.76826145356509656)]

################################################################################
Tune feature space by optimizing the model score
with cross validation. Model scores are R^2,
the coefficient of determination.
Progress: 1% 13% 37% 49% 61% 73% 86% 98% 

Time elapsed (sec) = 61.3

################################################################################
`important_features2`, `df_features4`:
    `df_features4` is a view into (not a copy) of `df_features3` with only
    `important_features2`. Feature importance is the normalized reduction
    in the loss score. A feature is selected as 'important' from the
    model score vs features plot.
    
`important_features2` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_78', 'VIN_fracReturnedasm1DivTransactions', 'DSEligible', 'pca_comp_75', 'pca_comp_73', 'pca_comp_76']

Cluster map of important feature correlations with heirarchical relationships.
    The deeper of the dendrogram node, the higher (anti)correlated the features are.
    The Spearman rank correlation accommodates non-linear features.
    The pair plot is a scatter matrix plot of columns vs each other.
    
Clustermap of target, 'Returned', top 10 important features, buyer_retrate_gt01:
Pairplot of target, 'Returned', top 5 important features, buyer_retrate_gt01:
Index(['Returned', 'VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm',
       'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull',
       'pca_comp_78', 'buyer_retrate_gt01'],
      dtype='object')

/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py:288: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[key] = _infer_fill_value(value)
/opt/conda/lib/python3.5/site-packages/pandas/core/indexing.py:465: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  self.obj[item] = s
/opt/demo/demo/app_predict/predict.py:1836: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  df_pairplot.drop([np.where(ds_columns.values == target)[0][0]], axis=1, inplace=True)
/opt/demo/demo/app_predict/predict.py:1837: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
  df_pairplot.drop([np.where(ds_columns.values == 'buyer_retrate_gt01')[0][0]], axis=1, inplace=True)

Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1   Returned_asm  \
count                          278337.000000  278337.000000   
mean                                0.072437       0.070095   
std                                 0.254448       0.255307   
min                                 0.000000       0.000000   
25%                                 0.000000       0.000000   
50%                                 0.000000       0.000000   
75%                                 0.000000       0.000000   
max                                 1.000000       1.000000   

       VIN_numReturnedNotNull  VIN_fracReturned1DivReturnedNotNull  \
count           278337.000000                        278337.000000   
mean                 0.079350                             0.020815   
std                  0.277162                             0.141990   
min                  0.000000                             0.000000   
25%                  0.000000                             0.000000   
50%                  0.000000                             0.000000   
75%                  0.000000                             0.000000   
max                  4.000000                             1.000000   

        pca_comp_78  
count  2.783370e+05  
mean   2.431927e-17  
std    8.707895e-03  
min   -2.556930e-01  
25%   -2.682069e-04  
50%    3.778332e-05  
75%    3.392039e-04  
max    2.741183e-01  

First 5 records for top 5 important features:
   VIN_fracReturnedNotNullDivDSEligible1  Returned_asm  \
0                                    0.0             0   
1                                    0.0             0   
2                                    0.0             0   
3                                    0.0             0   
4                                    0.0             0   

   VIN_numReturnedNotNull  VIN_fracReturned1DivReturnedNotNull  pca_comp_78  
0                       0                                  0.0     0.000569  
1                       0                                  0.0     0.000502  
2                       0                                  0.0     0.001204  
3                       0                                  0.0     0.000335  
4                       0                                  0.0     0.001024  

Describe top 5 important features. Format:
    Feature: importance score.
    Histogram of feature values.
VIN_fracReturnedNotNullDivDSEligible1:
    importance: 0.794
Returned_asm:
    importance: 0.106
VIN_numReturnedNotNull:
    importance: 0.041
VIN_fracReturned1DivReturnedNotNull:
    importance: 0.018
pca_comp_78:
    importance: 0.007
Time elapsed (sec) = 22.7

################################################################################
Tune model hyperparameters by optimizing the model score
    with cross validation. Model scores are R^2,
    the coefficient of determination.
    
Progress: 25% 50% 75% 100% 

Time elapsed (sec) = 13.0

################################################################################
Test significance of predictions by shuffling the target values.
    Model scores are r^2, the coefficient of determination.
    
Progress: 5% 25% 45% 65% 85% 

Average model score with shuffling: -0.147
Average model score without shuffling: 0.993

/opt/conda/lib/python3.5/site-packages/statsmodels/nonparametric/kdetools.py:20: VisibleDeprecationWarning: using a non-integer number instead of an integer will result in an error in the future
  y = X[:m/2+1] + np.r_[0,X[m/2+1:],0]*1j

Null hypothesis: There is no distinction in the differences
between the mean model scores whether or not the target
values have been shuffled.
Outcome: Assuming the null hypothesis, the probability of
obtaining a difference between the mean model scores at least
as great as 1.14 is 0.0%.

Time elapsed (sec) = 11.3

################################################################################
Predict target values with cross-validation,
    plot actual vs predicted and score.
    
Progress: 20% 40% 60% 80% 100% 

Model score = 0.995
`features.pkl`, `estimator.pkl`: Save features and estimator.
    
Time elapsed (sec) = 5.9

gc.collect()

19487

demo.utils.utils.plot_actual_vs_predicted(
    y_true=ds_target.values, y_pred=ds_predicted.values,
    loglog=False, xylims=(-1.1, 1.1),
    path=os.path.join(path_plot_dir, 'model_actual_vs_predicted.jpg'))

nftrs_pctls = np.asarray(
    [np.append(tup[0], np.percentile(tup[1], q=[5,50,95]))
     for tup in nftrs_scores])
plt.plot(
    nftrs_pctls[:, 0], nftrs_pctls[:, 2],
    marker='.', color=sns.color_palette()[0],
    label='50th pctl score')
plt.fill_between(
    nftrs_pctls[:, 0],
    y1=nftrs_pctls[:, 1],
    y2=nftrs_pctls[:, 3],
    alpha=0.5, color=sns.color_palette()[0],
    label='5-95th pctls of scores')
plt.title("Model score vs number of features")
plt.xlabel("Number of features")
plt.ylabel("Model score")
plt.legend(loc='upper left')
plt.savefig(os.path.join(path_plot_dir, 'model_tune_nfeatures.png'), dpi=300)
plt.show()

path_plot_dir = os.path.join(path_data_dir, 'plot_model')
df_importances = demo.utils.utils.calc_feature_importances(
    estimator=estimator, df_features=df_features2, ds_target=ds_target,
    replace=False, show_progress=True, show_plot=True, path=os.path.join(path_plot_dir, 'model_feature_importances.png'))

Progress: 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

# To make an example random forest tree
path_plot_dir = os.path.join(path_data_dir, 'plot_model')
path_dot = os.path.join(path_plot_dir, 'model_decision_tree.dot')
print("Graphviz dot and SVG files =\n{path}\n{path}.svg".format(path=path_dot))
sk_tr.export_graphviz(
    decision_tree=estimator.estimators_[0], out_file=path_dot,
    feature_names=df_features4.columns)
cmd = ['dot', '-Tsvg', path_dot, '-O']
subprocess.run(args=cmd, check=True)
cmd = ['dot', '-Tjpng', path_dot, '-O']
subprocess.run(args=cmd, check=True)
display(SVG(filename=path_dot+'.svg'))

Graphviz dot and SVG files =
/opt/demo/demo/app_predict/data/plot_model/model_decision_tree.dot
/opt/demo/demo/app_predict/data/plot_model/model_decision_tree.dot.svg

# data raw, data informative, data pca-kmeans, data feature importance
df_backup.shape, df_features.shape, df_features2.shape, df_features3.shape, df_features4.shape

((278337, 25), (278337, 89), (278337, 184), (278337, 59), (278337, 10))

Test pipeline model¶

df['RiskyDealerScore'] = demo.app_predict.predict.create_pipeline_model_new_data(df=df, path_data_dir=path_data_dir, show_plots=False)

################################################################################
Features:
['Arbitrated', 'Autocheck_score', 'BuyerID_fracDSEligible1DivTransactions', 'BuyerID_fracReturned1DivReturnedNotNull', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_fracReturnedasm1DivTransactions', 'BuyerID_numDSEligible1', 'BuyerID_numReturned1', 'BuyerID_numReturnedNotNull', 'BuyerID_numReturnedasm1', 'BuyerID_numTransactions', 'CarMake_fracDSEligible1DivTransactions', 'CarMake_fracReturned1DivReturnedNotNull', 'CarMake_fracReturnedNotNullDivDSEligible1', 'CarMake_fracReturnedasm1DivTransactions', 'CarMake_numDSEligible1', 'CarMake_numReturned1', 'CarMake_numReturnedNotNull', 'CarMake_numReturnedasm1', 'CarMake_numTransactions', 'CarYear', 'ConditionReport', 'DSEligible', 'InLane', 'JDPowersCat_COMPACTCAR', 'JDPowersCat_EXCLUDED', 'JDPowersCat_FULLSIZECAR', 'JDPowersCat_LUXURYCAR', 'JDPowersCat_MIDSIZECAR', 'JDPowersCat_PICKUP', 'JDPowersCat_SPORTSCAR', 'JDPowersCat_SUV', 'JDPowersCat_UNKNOWN', 'JDPowersCat_VAN', 'JDPowersCat_fracDSEligible1DivTransactions', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'JDPowersCat_fracReturnedasm1DivTransactions', 'JDPowersCat_numDSEligible1', 'JDPowersCat_numReturned1', 'JDPowersCat_numReturnedNotNull', 'JDPowersCat_numReturnedasm1', 'JDPowersCat_numTransactions', 'LIGHTG', 'LIGHTR', 'LIGHTY', 'LIGHT_N0G1Y2R3', 'MMR', 'Mileage', 'OVE', 'PSI', 'PSIEligible', 'Returned_asm', 'SaleDate_day', 'SaleDate_decyear', 'SaleDate_dow', 'SaleDate_doy', 'SalePrice', 'Salvage', 'SellerID_fracDSEligible1DivTransactions', 'SellerID_fracReturned1DivReturnedNotNull', 'SellerID_fracReturnedNotNullDivDSEligible1', 'SellerID_fracReturnedasm1DivTransactions', 'SellerID_numDSEligible1', 'SellerID_numReturned1', 'SellerID_numReturnedNotNull', 'SellerID_numReturnedasm1', 'SellerID_numTransactions', 'SellingLocation_fracDSEligible1DivTransactions', 'SellingLocation_fracReturned1DivReturnedNotNull', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellingLocation_fracReturnedasm1DivTransactions', 'SellingLocation_lat', 'SellingLocation_lon', 'SellingLocation_numDSEligible1', 'SellingLocation_numReturned1', 'SellingLocation_numReturnedNotNull', 'SellingLocation_numReturnedasm1', 'SellingLocation_numTransactions', 'Simulcast', 'VIN_fracDSEligible1DivTransactions', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'VIN_fracReturnedasm1DivTransactions', 'VIN_numDSEligible1', 'VIN_numReturned1', 'VIN_numReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_numTransactions']

################################################################################
`Container`: Create an empty container class and
dynamically allocate attributes to hold variables for specific steps
of the pipeline. 
`step.s0.[df,ds]_[features,target]`: Save initial state of features, target.

################################################################################
`transformer_scaler`, `transformer_pca`: Load existing transformers.

Time elapsed (sec) = 0.5

################################################################################
`transformer_kmeans`, `transformer_kmeans_pca`:
Predict centroid clusters.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
    
Time elapsed (sec) = 0.5

################################################################################
`df_features2`: Combine `df_features` with
cluster labels, cluster distances, PCA components, PCA cluster labels,
and PCA cluster distances into `df_features`.
Time elapsed (sec) = 1.3

################################################################################
Predict target values,
    plot actual vs predicted and score.
    
`features.pkl`, `estimator.pkl`: Save features and estimator.
    
Model score = 0.999
Time elapsed (sec) = 2.5

demo.app_predict.predict.plot_model(df=df, path_plot_dir=path_plot_dir)

buyerid = '18584'
col = 'Returned'
print('buyerid:', buyerid)
sns.distplot(df.loc[df['BuyerID']==buyerid, col], kde=False)
plt.title(col+' frequency distribution\nfor BuyerID='+buyerid)
plt.xlabel(col)
plt.ylabel('Number of transactions\nwith '+col+' = X')
plt.savefig(
    os.path.join(path_data_dir, 'plot_model', 'model_returned-freq-dist_for_'+buyerid+'_before.png'),
    dpi=300)
print(df.loc[df['BuyerID']==buyerid, col].mean(),
      df.loc[df['BuyerID']==buyerid, col].std())

buyerid: 18584
0.010752688172 0.697402495844

buyerid = '18584'
col = 'Returned'
print('buyerid:', buyerid)
sns.distplot(df_modl.loc[df_modl['BuyerID']==buyerid, col], kde=False)
plt.title(col+' frequency distribution\nfor BuyerID='+buyerid)
plt.xlabel(col)
plt.ylabel('Number of transactions\nwith '+col+' = X')
plt.savefig(
    os.path.join(path_data_dir, 'plot_model', 'model_returned-freq-dist_for_'+buyerid+'_after.png'),
    dpi=300)
print(df_modl.loc[df_modl['BuyerID']==buyerid, col].mean(),
      df_modl.loc[df_modl['BuyerID']==buyerid, col].std())

buyerid: 18584
-0.210810810811 0.662580505274

buyerid = '272356'
col = 'Returned'
print('buyerid:', buyerid)
sns.distplot(df.loc[df['BuyerID']==buyerid, col], kde=False)
plt.title(col+' frequency distribution\nfor BuyerID='+buyerid)
plt.xlabel(col)
plt.ylabel('Number of transactions\nwith '+col+' = X')
plt.savefig(
    os.path.join(path_data_dir, 'plot_model', 'model_returned-freq-dist_for_'+buyerid+'_before.png'),
    dpi=300)
print(df.loc[df['BuyerID']==buyerid, col].mean(),
      df.loc[df['BuyerID']==buyerid, col].std())

buyerid: 272356
-0.495284552846 0.561356298

buyerid = '272356'
col = 'Returned'
print('buyerid:', buyerid)
sns.distplot(df_modl.loc[df_modl['BuyerID']==buyerid, col], kde=False)
plt.title(col+' frequency distribution\nfor BuyerID='+buyerid)
plt.xlabel(col)
plt.ylabel('Number of transactions\nwith '+col+' = X')
plt.savefig(
    os.path.join(path_data_dir, 'plot_model', 'model_returned-freq-dist_for_'+buyerid+'_after.png'),
    dpi=300)
print(df_modl.loc[df_modl['BuyerID']==buyerid, col].mean(),
      df_modl.loc[df_modl['BuyerID']==buyerid, col].std())

buyerid: 272356
-0.519349593496 0.539162070427

buyerid = '328701'
col = 'Returned'
print('buyerid:', buyerid)
sns.distplot(df.loc[df['BuyerID']==buyerid, col], kde=False)
plt.title(col+' frequency distribution\nfor BuyerID='+buyerid)
plt.xlabel(col)
plt.ylabel('Number of transactions\nwith '+col+' = X')
plt.savefig(
    os.path.join(path_data_dir, 'plot_model', 'model_returned-freq-dist_for_'+buyerid+'_before.png'),
    dpi=300)
print(df.loc[df['BuyerID']==buyerid, col].mean(),
      df.loc[df['BuyerID']==buyerid, col].std())

buyerid: 328701
0.329966329966 0.787708144229

buyerid = '328701'
col = 'Returned'
print('buyerid:', buyerid)
sns.distplot(df_modl.loc[df_modl['BuyerID']==buyerid, col], kde=False)
plt.title(col+' frequency distribution\nfor BuyerID='+buyerid)
plt.xlabel(col)
plt.ylabel('Number of transactions\nwith '+col+' = X')
plt.savefig(
    os.path.join(path_data_dir, 'plot_model', 'model_returned-freq-dist_for_'+buyerid+'_after.png'),
    dpi=300)
print(df_modl.loc[df_modl['BuyerID']==buyerid, col].mean(),
      df_modl.loc[df_modl['BuyerID']==buyerid, col].std())

buyerid: 328701
-0.3367003367 0.784843688883

# For by-buyers
# remember_buyers = True
date_freq = 'W'

date_range = pd.date_range(start=df['SaleDate'].min(), end=df['SaleDate'].max(), freq=date_freq)
df_modl = df.loc[
    np.logical_and(
        date_range[0] <= df['SaleDate'],
        df['SaleDate'] < date_range[1])].copy()
df_orig = df.loc[
    np.logical_and(
        date_range[0] <= df['SaleDate'],
        df['SaleDate'] < date_range[1])].copy()
buyers = np.asarray([])
buyers_prohibited = dict() # key: (df_train['SaleDate'].min(), df_train['SaleDate'].max())
transactions_affected = dict() # key: (df_test['SaleDate'].min(), df_test['SaleDate'].max())
retrates_modl_chunk = dict() # key: (df_test['SaleDate'].min(), df_test['SaleDate'].max())
retrates_orig_chunk = dict() # key: (df_eval['SaleDate'].min(), df_eval['SaleDate'].max())
retrates_modl_all = dict() # key: (df_modl['SaleDate'].min(), df_modl['SaleDate'].max())
retrates_orig_all = dict() # key: (df_orig['SaleDate'].min(), df_orig['SaleDate'].max())

for idx in range(len(date_range)-2):
    print('#'*40)
    print('Timestamp:', time.strftime(r'%Y-%m-%dT%H:%M:%S%Z', time.gmtime()))
    
    # Define data sets.
    (saledate_train_min, saledate_train_max) = (date_range[idx],   date_range[idx+1])
    (saledate_test_min,  saledate_test_max)  = (date_range[idx+1], date_range[idx+2])
    (saledate_eval_min,  saledate_eval_max)  = (date_range[idx+1], date_range[idx+2])
    df_train = df_modl.loc[
        np.logical_and(
            saledate_train_min <= df_modl['SaleDate'],
            df_modl['SaleDate'] < saledate_train_max)].copy()
    df_test = df.loc[
        np.logical_and(
            saledate_test_min <= df['SaleDate'],
            df['SaleDate'] < saledate_test_max)].copy()
    df_eval = df.loc[
        np.logical_and(
            saledate_eval_min <= df['SaleDate'],
            df['SaleDate'] < saledate_eval_max)].copy()
    
    # Train model on old data.
    demo.app_predict.predict.create_pipeline_model(
        df=df_train, path_data_dir=path_data_dir, show_plots=False)
    # Use existing model to predict RiskyDealerScore for new data.
    # RiskyDealerScore is predictions of Returned.
    ds_rds = demo.app_predict.predict.create_pipeline_model_new_data(
        df=df_test, path_data_dir=path_data_dir, show_plots=False)    

    #     # For by-buyer
    #     # Prohibit purchase of DealShield in test data if BuyerID has predicted mean(RiskyDealerScore) > 0:
    #     # If prohibiting purchase of DealShield,
    #     # then for prohibed buyers, set DSEligible = 0 and Returned = -1 in the test data.
    #     # Do not remember buyers_prohibited.
    #     df_tmp = df_train.append(df_test)
    #     df_tmp.loc[ds_rds.index, 'Returned'] = ds_rds
    #     buyer_rds = df_tmp[['BuyerID', 'Returned']].groupby(by='BuyerID').mean()
    #     if remember_buyers:
    #         buyers = np.unique(
    #             np.append(
    #                 buyers,
    #                 buyer_rds.loc[buyer_rds['Returned'] > 0].index.values))
    #     else:
    #         buyers = buyer_rds.loc[buyer_rds['Returned'] > 0].index.values
    #     buyers_prohibited[(df_train['SaleDate'].min(), df_train['SaleDate'].max())] = buyers
    #     tfmask = np.logical_and(
    #         np.logical_and(
    #             df_test['SaleDate'] > df_train['SaleDate'].max(),
    #             df_test['BuyerID'].isin(buyers)),
    #         df_test['DSEligible'] == 1)    
    # For by-transaction
    # Prohibit purchase of DealShield in test data if transaction has predicted RiskyDealerScore > 0:
    # If prohibiting purchase of DealShield,
    # Then for prohibited transactions, set DSEligible = 0 and Returned = -1 in the test data.
    tfmask = ds_rds > 0
    transactions_affected[(df_test['SaleDate'].min(), df_test['SaleDate'].max())] = tfmask.loc[tfmask].index
    df_test.loc[tfmask, 'DSEligible'] = 0
    df_test.loc[tfmask, 'Returned'] = -1

    # Update calculated features.
    df_modl = demo.app_predict.predict.update_features_append(
        df_prev=df_modl, df_next=df_test, debug=False)
    df_orig = df_orig.append(df_eval)
    
    # Calculate chunk return rates.
    df_chunk = df_modl.loc[
        np.logical_and(
            saledate_test_min <= df_modl['SaleDate'],
            df_modl['SaleDate'] < saledate_test_max)]
    retrate_modl = sum(df_chunk['Returned']==1)/sum(df_chunk['Returned']!=-1)
    df_chunk = df_orig.loc[
        np.logical_and(
            saledate_eval_min <= df_orig['SaleDate'],
            df_orig['SaleDate'] < saledate_eval_max)]
    retrate_orig = sum(df_chunk['Returned']==1)/sum(df_chunk['Returned']!=-1)
    retrates_modl_chunk[(saledate_test_min, saledate_test_max)] = retrate_modl
    retrates_orig_chunk[(saledate_eval_min, saledate_eval_max)] = retrate_orig
    print('Chunk:')
    print('Evaluation time span:', df_chunk['SaleDate'].min(), df_chunk['SaleDate'].max())
    print('Model return rate:   ', retrate_modl)
    print('Original return rate:', retrate_orig)

    # Calculate overall return rates.
    retrate_modl = sum(df_modl['Returned']==1)/sum(df_modl['Returned']!=-1)
    retrate_orig = sum(df_orig['Returned']==1)/sum(df_orig['Returned']!=-1)
    retrates_modl_all[(df_modl['SaleDate'].min(), df_modl['SaleDate'].max())] = retrate_modl
    retrates_orig_all[(df_orig['SaleDate'].min(), df_orig['SaleDate'].max())] = retrate_orig
    print('Overall:')
    print('Evaluation time span:', df_orig['SaleDate'].min(), df_orig['SaleDate'].max())
    print('Model return rate:   ', retrate_modl)
    print('Original return rate:', retrate_orig)
    gc.collect()

########################################
Timestamp: 2017-03-02T10:06:28GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
[]
Summarize top 5 important features:
important_features2 is empty
Important features list is empty.
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 1.000
Chunk:
Evaluation time span: 2013-02-10 00:00:00 2013-02-16 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-02-16 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-03-02T10:06:31GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
[]
Summarize top 5 important features:
important_features2 is empty
Important features list is empty.
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 1.000
Chunk:
Evaluation time span: 2013-02-17 00:00:00 2013-02-23 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-02-23 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-03-02T10:06:35GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
[]
Summarize top 5 important features:
important_features2 is empty
Important features list is empty.
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 1.000
Chunk:
Evaluation time span: 2013-02-24 00:00:00 2013-03-02 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-02 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-03-02T10:06:38GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
[]
Summarize top 5 important features:
important_features2 is empty
Important features list is empty.
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 1.000
Chunk:
Evaluation time span: 2013-03-03 00:00:00 2013-03-09 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-09 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-03-02T10:06:42GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
[]
Summarize top 5 important features:
important_features2 is empty
Important features list is empty.
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 1.000
Chunk:
Evaluation time span: 2013-03-10 00:00:00 2013-03-16 00:00:00
Model return rate:    nan
Original return rate: nan
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-16 00:00:00
Model return rate:    nan
Original return rate: nan
########################################
Timestamp: 2017-03-02T10:06:45GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
[]
Summarize top 5 important features:
important_features2 is empty
Important features list is empty.
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = -0.001
Chunk:
Evaluation time span: 2013-03-17 00:00:00 2013-03-23 00:00:00
Model return rate:    0.0
Original return rate: 0.0
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-23 00:00:00
Model return rate:    0.0
Original return rate: 0.0
########################################
Timestamp: 2017-03-02T10:06:49GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'pca_comp_48', 'pca_comp_43']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             6305.000000                            6305.000000   
mean                 0.000952                               0.000952   
std                  0.030836                               0.030836   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

        pca_comp_48   pca_comp_43  
count  6.305000e+03  6.305000e+03  
mean   1.800997e-18  3.673597e-18  
std    3.204550e-02  9.135607e-02  
min   -5.603634e-01 -2.859878e-01  
25%   -1.794282e-03 -1.824042e-02  
50%    1.207841e-03 -5.065691e-03  
75%    4.113957e-03  7.370712e-03  
max    7.194844e-01  2.257620e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 1.000
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.874
Chunk:
Evaluation time span: 2013-03-25 00:00:00 2013-03-30 00:00:00
Model return rate:    0.0
Original return rate: 0.0
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-03-30 00:00:00
Model return rate:    0.0
Original return rate: 0.0
########################################
Timestamp: 2017-03-02T10:06:54GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'pca_comp_42', 'pca_comp_48', 'pca_comp_37', 'VIN_numDSEligible1', 'pca_comp_54', 'VIN_numTransactions', 'pca_comp_51', 'pca_comp_41', 'SellerID_fracReturnedNotNullDivDSEligible1', 'SellerID_numDSEligible1', 'SellerID_numReturnedNotNull', 'SellingLocation_numReturnedasm1', 'pca_comp_29', 'pca_comp_03', 'pca_comp_11', 'pca_comp_53', 'pca_comp_30', 'pca_comp_35', 'pca_comp_36', 'pca_comp_38', 'pca_comp_49', 'BuyerID_numReturnedNotNull']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5667.000000             5667.000000   
mean                                0.001147                0.001235   
std                                 0.033193                0.035127   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                1.000000   

        pca_comp_42   pca_comp_48   pca_comp_37  
count  5.667000e+03  5.667000e+03  5.667000e+03  
mean   1.088771e-17 -8.494543e-20 -8.878405e-18  
std    1.068222e-01  3.595365e-02  1.933022e-01  
min   -2.719575e-01 -6.844153e-01 -7.311452e-01  
25%   -3.173419e-02 -1.569564e-03 -5.942529e-02  
50%   -8.505077e-03  1.746450e-03 -9.108867e-03  
75%    1.450669e-02  4.591372e-03  4.494162e-02  
max    3.082570e+00  9.875722e-01  2.116968e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.933
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.915
Chunk:
Evaluation time span: 2013-03-31 00:00:00 2013-04-06 00:00:00
Model return rate:    0.0263157894737
Original return rate: 0.0263157894737
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-06 00:00:00
Model return rate:    0.02
Original return rate: 0.02
########################################
Timestamp: 2017-03-02T10:06:59GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_60', 'BuyerID_numReturned1', 'pca_comp_56', 'SellerID_fracReturned1DivReturnedNotNull', 'SellerID_numReturned1', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_62', 'pca_comp_52', 'VIN_numReturned1', 'VIN_numReturnedasm1', 'pca_comp_29', 'pca_comp_53', 'pca_comp_61', 'CarMake_fracReturned1DivReturnedNotNull', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'pca_comp_57', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_44']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             5606.000000                            5606.000000   
mean                 0.006778                               0.006422   
std                  0.082059                               0.078760   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

       Returned_asm  BuyerID_fracReturned1DivReturnedNotNull   pca_comp_60  
count   5606.000000                              5606.000000  5.606000e+03  
mean       0.052265                                 0.000178  4.017610e-17  
std        0.222582                                 0.009443  1.193985e-02  
min        0.000000                                 0.000000 -5.619663e-01  
25%        0.000000                                 0.000000 -1.621865e-03  
50%        0.000000                                 0.000000  6.944911e-06  
75%        0.000000                                 0.000000  1.739356e-03  
max        1.000000                                 0.500000  2.592061e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.975
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 1.000
Chunk:
Evaluation time span: 2013-04-07 00:00:00 2013-04-13 00:00:00
Model return rate:    0.0
Original return rate: 0.0
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-13 00:00:00
Model return rate:    0.0103092783505
Original return rate: 0.0103092783505
########################################
Timestamp: 2017-03-02T10:07:05GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1
count             6090.000000                            6090.000000
mean                 0.007718                               0.007635
std                  0.087517                               0.086818
min                  0.000000                               0.000000
25%                  0.000000                               0.000000
50%                  0.000000                               0.000000
75%                  0.000000                               0.000000
max                  1.000000                               1.000000
Progress: 20% 40% 60% 80% 100% 

Model score = 0.998
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.928
Chunk:
Evaluation time span: 2013-04-14 00:00:00 2013-04-19 00:00:00
Model return rate:    0.0535714285714
Original return rate: 0.0535714285714
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-19 00:00:00
Model return rate:    0.0261437908497
Original return rate: 0.0261437908497
########################################
Timestamp: 2017-03-02T10:07:10GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_fracReturnedasm1DivTransactions', 'SellerID_numReturned1', 'Returned_asm', 'VIN_numReturned1', 'VIN_numDSEligible1', 'pca_comp_40', 'VIN_numTransactions', 'pca_comp_42', 'pca_comp_57', 'pca_comp_62', 'LIGHTG', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_45', 'pca_comp_46', 'pca_comp_56', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_13', 'pca_comp_38', 'pca_comp_58', 'pca_comp_35', 'pca_comp_26', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_16', 'SalePrice', 'CarMake_fracReturned1DivReturnedNotNull', 'JDPowersCat_fracDSEligible1DivTransactions', 'JDPowersCat_MIDSIZECAR', 'pca_comp_61', 'SellerID_numDSEligible1', 'SellerID_numTransactions', 'CarMake_fracReturnedasm1DivTransactions', 'CarYear', 'pca_comp_02', 'pca_comp_17', 'pca_comp_68', 'pca_comp_20', 'pca_comp_53', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_63', 'pca_comp_59', 'JDPowersCat_fracReturnedasm1DivTransactions', 'pca_comp_39', 'pca_comp_36', 'pca_comp_19', 'pca_comp_11', 'pca_comp_07', 'pca_comp_43', 'LIGHT_N0G1Y2R3', 'pca_comp_34', 'JDPowersCat_numReturnedasm1', 'CarMake_numDSEligible1', 'pca_comp_70', 'SellingLocation_fracReturnedasm1DivTransactions', 'pca_comp_44', 'pca_comp_51', 'pca_comp_08', 'BuyerID_fracDSEligible1DivTransactions']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5720.000000             5720.000000   
mean                                0.009965                0.010315   
std                                 0.098451                0.101045   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                1.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturnedasm1  \
count                          5720.000000          5720.000000   
mean                              0.000524             0.052797   
std                               0.022897             0.225981   
min                               0.000000             0.000000   
25%                               0.000000             0.000000   
50%                               0.000000             0.000000   
75%                               0.000000             0.000000   
max                               1.000000             2.000000   

       VIN_fracReturnedasm1DivTransactions  
count                          5720.000000  
mean                              0.051573  
std                               0.220392  
min                               0.000000  
25%                               0.000000  
50%                               0.000000  
75%                               0.000000  
max                               1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.968
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.932
Chunk:
Evaluation time span: 2013-04-21 00:00:00 2013-04-27 00:00:00
Model return rate:    0.0
Original return rate: 0.0576923076923
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-04-27 00:00:00
Model return rate:    0.02
Original return rate: 0.0341463414634
########################################
Timestamp: 2017-03-02T10:07:16GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'pca_comp_44', 'pca_comp_27', 'pca_comp_06', 'pca_comp_37', 'pca_comp_63', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'VIN_numDSEligible1', 'pca_comp_60', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_19', 'pca_comp_66', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_12', 'BuyerID_numReturnedNotNull', 'pca_comp_17', 'pca_comp_04', 'pca_comp_01', 'pca_comp_05', 'pca_comp_65', 'SalePrice', 'pca_comp_02', 'pca_comp_16', 'pca_comp_22', 'pca_comp_35', 'VIN_numTransactions', 'pca_comp_24', 'ConditionReport', 'pca_comp_00', 'pca_comp_36', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_34', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'pca_comp_49', 'pca_cluster_1_dist', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_47', 'pca_comp_31', 'pca_comp_30', 'pca_comp_64', 'SellingLocation_numReturnedasm1', 'MMR', 'CarMake_fracReturnedasm1DivTransactions', 'pca_comp_28', 'JDPowersCat_numReturned1', 'CarMake_numDSEligible1', 'CarMake_numTransactions', 'pca_comp_11', 'CarMake_fracReturnedNotNullDivDSEligible1', 'Autocheck_score', 'JDPowersCat_fracDSEligible1DivTransactions', 'SellingLocation_numReturnedNotNull', 'pca_cluster_0_dist', 'pca_comp_68', 'pca_comp_20', 'pca_comp_71', 'pca_comp_38']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             6074.000000                            6074.000000   
mean                 0.008232                               0.007601   
std                  0.090363                               0.085236   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

        pca_comp_44   pca_comp_27   pca_comp_06  
count  6.074000e+03  6.074000e+03  6.074000e+03  
mean  -7.522428e-17  2.805488e-17 -2.402681e-17  
std    1.067775e-01  4.031969e-01  2.309402e+00  
min   -8.689928e-01 -2.956313e+00 -1.694126e+01  
25%   -2.626192e-02 -1.180146e-01 -3.569849e-01  
50%    2.941517e-04 -4.050110e-03  7.012193e-01  
75%    2.743399e-02  1.578955e-01  1.166400e+00  
max    1.281656e+00  1.778804e+00  1.337341e+01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.979
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.862
Chunk:
Evaluation time span: 2013-04-28 00:00:00 2013-05-04 00:00:00
Model return rate:    0.0816326530612
Original return rate: 0.0816326530612
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-04 00:00:00
Model return rate:    0.0321285140562
Original return rate: 0.0433070866142
########################################
Timestamp: 2017-03-02T10:07:22GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_fracReturnedasm1DivTransactions', 'VIN_numReturnedasm1', 'Returned_asm', 'VIN_numReturned1', 'VIN_numTransactions', 'JDPowersCat_COMPACTCAR', 'pca_comp_64', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'VIN_numDSEligible1', 'pca_comp_38', 'pca_comp_62', 'pca_comp_57', 'pca_comp_41', 'pca_comp_65', 'pca_comp_60', 'pca_comp_46', 'pca_comp_55', 'pca_comp_26', 'pca_comp_09', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_61', 'pca_comp_45', 'LIGHTG', 'pca_comp_39', 'pca_comp_28', 'pca_comp_10', 'pca_comp_25', 'pca_comp_50', 'SellerID_numReturnedNotNull', 'pca_comp_13', 'pca_comp_21', 'SellerID_numDSEligible1', 'pca_comp_48', 'pca_comp_40', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_17', 'pca_comp_53', 'Autocheck_score', 'SellingLocation_numTransactions', 'pca_comp_11', 'pca_comp_49', 'CarYear', 'pca_comp_52', 'pca_comp_58']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5565.000000             5565.000000   
mean                                0.009224                0.010063   
std                                 0.093496                0.099817   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                1.000000   

       VIN_fracReturned1DivReturnedNotNull  \
count                          5565.000000   
mean                              0.000719   
std                               0.026803   
min                               0.000000   
25%                               0.000000   
50%                               0.000000   
75%                               0.000000   
max                               1.000000   

       VIN_fracReturnedasm1DivTransactions  VIN_numReturnedasm1  
count                          5565.000000          5565.000000  
mean                              0.045103             0.045642  
std                               0.206899             0.208727  
min                               0.000000             0.000000  
25%                               0.000000             0.000000  
50%                               0.000000             0.000000  
75%                               0.000000             0.000000  
max                               1.000000             1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.988
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.944
Chunk:
Evaluation time span: 2013-05-05 00:00:00 2013-05-11 00:00:00
Model return rate:    0.0462962962963
Original return rate: 0.274647887324
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-11 00:00:00
Model return rate:    0.0364145658263
Original return rate: 0.126262626263
########################################
Timestamp: 2017-03-02T10:07:29GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'PSIEligible', 'Returned_asm', 'VIN_numReturnedasm1', 'VIN_fracReturnedasm1DivTransactions', 'VIN_numTransactions', 'pca_comp_62', 'cluster_1_dist', 'pca_cluster_0_dist', 'pca_comp_37', 'VIN_numDSEligible1', 'pca_comp_60', 'pca_comp_58', 'pca_comp_53', 'pca_comp_61', 'pca_comp_30', 'pca_comp_32', 'pca_comp_26', 'pca_comp_36', 'MMR', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_49', 'pca_comp_13', 'pca_comp_68', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_40', 'pca_comp_54', 'pca_comp_11', 'SalePrice', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_22', 'BuyerID_numReturnedNotNull', 'pca_comp_04', 'pca_comp_56', 'BuyerID_numReturned1', 'BuyerID_numDSEligible1', 'LIGHT_N0G1Y2R3', 'pca_comp_66', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_59', 'pca_comp_48', 'pca_comp_12', 'pca_cluster_1_dist']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6193.000000             6193.000000   
mean                                0.017116                0.018892   
std                                 0.126599                0.137337   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturned1  PSIEligible  
count                          6193.000000       6193.000000  6193.000000  
mean                              0.000807          0.000807     0.717261  
std                               0.028405          0.028405     0.450367  
min                               0.000000          0.000000     0.000000  
25%                               0.000000          0.000000     0.000000  
50%                               0.000000          0.000000     1.000000  
75%                               0.000000          0.000000     1.000000  
max                               1.000000          1.000000     1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.993
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.988
Chunk:
Evaluation time span: 2013-05-12 00:00:00 2013-05-18 00:00:00
Model return rate:    0.0
Original return rate: 0.269662921348
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-18 00:00:00
Model return rate:    0.0308056872038
Original return rate: 0.152577319588
########################################
Timestamp: 2017-03-02T10:07:35GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'pca_comp_39', 'pca_comp_59', 'VIN_numDSEligible1', 'VIN_numTransactions', 'SellingLocation_lat', 'SellingLocation_lon', 'SaleDate_decyear', 'SellingLocation_numReturned1', 'pca_comp_35', 'SellingLocation_numReturnedNotNull', 'pca_comp_34', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SaleDate_dow', 'SellingLocation_numReturnedasm1', 'SellingLocation_numTransactions', 'SellingLocation_fracDSEligible1DivTransactions', 'SaleDate_doy', 'LIGHTR', 'SellerID_numReturned1', 'pca_comp_26', 'LIGHT_N0G1Y2R3', 'pca_comp_41', 'cluster_0_dist', 'SellingLocation_fracReturnedasm1DivTransactions', 'MMR', 'PSIEligible', 'CarYear', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_14', 'pca_comp_62', 'pca_comp_03', 'pca_comp_11', 'BuyerID_numReturnedNotNull', 'Autocheck_score', 'pca_comp_60', 'Mileage', 'SaleDate_day', 'SalePrice', 'pca_comp_19', 'BuyerID_fracDSEligible1DivTransactions', 'SellingLocation_numDSEligible1', 'pca_comp_15']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5892.000000             5892.000000   
mean                                0.011301                0.012220   
std                                 0.103758                0.109876   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                1.000000   

        pca_comp_39   pca_comp_59  VIN_numDSEligible1  
count  5.892000e+03  5.892000e+03         5892.000000  
mean  -7.553054e-18  3.145844e-17            0.969280  
std    1.405811e-01  1.393887e-02            0.292334  
min   -7.222085e-01 -3.834046e-01            0.000000  
25%   -3.100796e-02 -1.797219e-03            1.000000  
50%   -7.999452e-03 -5.371319e-04            1.000000  
75%    1.288353e-02  8.922595e-04            1.000000  
max    1.658963e+00  9.674088e-02            4.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 1.000
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.850
Chunk:
Evaluation time span: 2013-05-19 00:00:00 2013-05-25 00:00:00
Model return rate:    0.185567010309
Original return rate: 0.185567010309
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-25 00:00:00
Model return rate:    0.0795454545455
Original return rate: 0.162002945508
########################################
Timestamp: 2017-03-02T10:07:41GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'pca_comp_62', 'VIN_numDSEligible1', 'pca_comp_63', 'pca_comp_35', 'VIN_numTransactions', 'pca_comp_66', 'SellerID_numReturned1', 'SellerID_fracReturned1DivReturnedNotNull', 'SellerID_numReturnedNotNull', 'SellingLocation_fracDSEligible1DivTransactions', 'SellingLocation_numReturnedNotNull', 'pca_comp_07', 'SellingLocation_fracReturnedasm1DivTransactions', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellingLocation_numReturned1', 'pca_comp_14', 'pca_comp_18', 'pca_comp_12', 'pca_comp_31', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_01', 'pca_comp_55', 'pca_comp_40', 'cluster_0_dist', 'SellerID_fracReturnedNotNullDivDSEligible1', 'SellerID_fracDSEligible1DivTransactions', 'SaleDate_doy', 'JDPowersCat_numReturned1']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             5830.000000                            5830.000000   
mean                 0.034991                               0.033419   
std                  0.183774                               0.177597   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

       Returned_asm   pca_comp_62  VIN_numDSEligible1  
count   5830.000000  5.830000e+03         5830.000000  
mean       0.054717  7.200739e-20            0.981990  
std        0.227446  2.133689e-02            0.282475  
min        0.000000 -4.286332e-01            0.000000  
25%        0.000000 -1.693213e-03            1.000000  
50%        0.000000 -6.199982e-04            1.000000  
75%        0.000000  5.975842e-04            1.000000  
max        1.000000  2.311126e-01            3.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 1.000
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.991
Chunk:
Evaluation time span: 2013-05-26 00:00:00 2013-05-31 00:00:00
Model return rate:    0.0
Original return rate: 0.298780487805
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-05-31 00:00:00
Model return rate:    0.0670314637483
Original return rate: 0.188612099644
########################################
Timestamp: 2017-03-02T10:07:48GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'pca_comp_58', 'VIN_numTransactions', 'pca_comp_57', 'pca_comp_35', 'VIN_numDSEligible1', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_37', 'pca_comp_52', 'pca_comp_47', 'pca_comp_53', 'pca_comp_38', 'pca_comp_48', 'pca_comp_49', 'BuyerID_numTransactions', 'BuyerID_numDSEligible1', 'pca_comp_17', 'pca_comp_01', 'pca_comp_04', 'pca_comp_16', 'pca_comp_54', 'pca_comp_65', 'pca_comp_55', 'pca_comp_03', 'pca_comp_09', 'VIN_numReturned1', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_08', 'pca_comp_50', 'pca_comp_61', 'pca_comp_68', 'CarMake_fracReturnedasm1DivTransactions', 'CarMake_fracDSEligible1DivTransactions']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             4901.000000                            4901.000000   
mean                 0.024281                               0.023295   
std                  0.153935                               0.149361   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

        pca_comp_58  VIN_numTransactions   pca_comp_57  
count  4.901000e+03          4901.000000  4.901000e+03  
mean  -2.741295e-17             1.032034 -3.025910e-16  
std    2.345620e-02             0.181811  2.492683e-02  
min   -5.172341e-01             1.000000 -2.294217e-01  
25%   -2.889731e-03             1.000000 -4.332962e-03  
50%   -7.620604e-04             1.000000 -7.799630e-04  
75%    1.693913e-03             1.000000  2.870260e-03  
max    1.769475e-01             3.000000  4.441703e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.981
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.743
Chunk:
Evaluation time span: 2013-06-02 00:00:00 2013-06-08 00:00:00
Model return rate:    0.30243902439
Original return rate: 0.30243902439
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-08 00:00:00
Model return rate:    0.11858974359
Original return rate: 0.210877862595
########################################
Timestamp: 2017-03-02T10:07:54GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_39', 'VIN_numDSEligible1', 'VIN_numTransactions', 'pca_comp_61', 'pca_comp_64', 'Mileage', 'pca_comp_60', 'BuyerID_numReturnedNotNull', 'SellingLocation_fracReturned1DivReturnedNotNull', 'pca_comp_16', 'pca_comp_67', 'CarMake_numReturned1', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_32', 'pca_comp_07', 'pca_comp_41', 'BuyerID_numDSEligible1', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_55', 'BuyerID_numTransactions', 'pca_comp_26', 'pca_comp_49', 'pca_comp_08', 'pca_comp_34', 'pca_comp_35', 'pca_comp_63', 'pca_comp_04', 'Arbitrated', 'pca_comp_30', 'Autocheck_score', 'pca_comp_70', 'pca_comp_13', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellingLocation_numReturnedNotNull', 'pca_comp_18', 'PSIEligible', 'pca_comp_21', 'pca_comp_23', 'pca_comp_20', 'JDPowersCat_fracDSEligible1DivTransactions', 'CarYear', 'CarMake_numReturnedasm1', 'CarMake_numReturnedNotNull', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_14', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_02', 'SellerID_fracReturnedNotNullDivDSEligible1', 'CarMake_fracReturnedNotNullDivDSEligible1', 'SellingLocation_numDSEligible1', 'pca_comp_51', 'pca_comp_58', 'pca_comp_47', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_00', 'BuyerID_numReturnedasm1', 'pca_comp_43', 'pca_comp_59', 'SaleDate_dow', 'LIGHTR', 'pca_comp_33', 'SellerID_numDSEligible1', 'SellerID_fracReturnedasm1DivTransactions', 'SellingLocation_lat', 'SellingLocation_numReturned1', 'pca_comp_36', 'PSI', 'SaleDate_doy', 'pca_comp_48', 'SellingLocation_fracReturnedasm1DivTransactions', 'pca_cluster_0_dist', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_09', 'pca_comp_25', 'CarMake_numTransactions']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             6361.000000                            6361.000000   
mean                 0.033485                               0.032005   
std                  0.179914                               0.174046   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

       Returned_asm  BuyerID_fracReturnedNotNullDivDSEligible1   pca_comp_39  
count   6361.000000                                6361.000000  6.361000e+03  
mean       0.065556                                   0.008813 -2.967111e-18  
std        0.247523                                   0.040236  2.261858e-01  
min        0.000000                                   0.000000 -1.114342e+00  
25%        0.000000                                   0.000000 -7.745039e-02  
50%        0.000000                                   0.000000 -1.436190e-02  
75%        0.000000                                   0.000000  4.729452e-02  
max        1.000000                                   1.000000  3.368160e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.997
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.982
Chunk:
Evaluation time span: 2013-06-10 00:00:00 2013-06-15 00:00:00
Model return rate:    0.0
Original return rate: 0.278195488722
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-15 00:00:00
Model return rate:    0.0984042553191
Original return rate: 0.224505327245
########################################
Timestamp: 2017-03-02T10:08:01GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numDSEligible1', 'BuyerID_numReturnedNotNull', 'pca_comp_35', 'pca_comp_37', 'pca_comp_05', 'pca_comp_36', 'VIN_fracReturned1DivReturnedNotNull', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_59', 'pca_comp_56', 'pca_comp_60', 'pca_comp_11', 'VIN_numReturned1', 'pca_comp_22', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_03', 'pca_comp_10', 'Autocheck_score', 'CarMake_fracDSEligible1DivTransactions', 'cluster_0_dist', 'pca_comp_15', 'VIN_numReturnedasm1', 'pca_comp_58', 'CarMake_fracReturnedasm1DivTransactions', 'pca_cluster_0_dist', 'pca_comp_41', 'pca_comp_31', 'pca_comp_54', 'Mileage', 'pca_comp_32', 'pca_comp_23', 'SellerID_numReturnedasm1', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_52', 'pca_comp_19', 'SellerID_fracReturned1DivReturnedNotNull', 'JDPowersCat_numReturnedNotNull', 'CarMake_numReturned1', 'pca_comp_65', 'pca_comp_33', 'CarMake_numReturnedNotNull', 'pca_comp_14', 'SellerID_numDSEligible1', 'pca_comp_27', 'pca_comp_43', 'SellerID_numTransactions', 'pca_comp_29', 'pca_comp_40', 'pca_comp_48', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_30', 'pca_comp_04', 'pca_comp_17', 'pca_comp_61', 'SellerID_fracReturnedasm1DivTransactions']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             6134.000000                            6134.000000   
mean                 0.033094                               0.030758   
std                  0.178897                               0.169364   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

       VIN_numDSEligible1  BuyerID_numReturnedNotNull   pca_comp_35  
count         6134.000000                 6134.000000  6.134000e+03  
mean             0.956798                    0.988099 -2.352934e-19  
std              0.308463                    3.925321  2.369513e-01  
min              0.000000                    0.000000 -1.014847e+00  
25%              1.000000                    0.000000 -1.442965e-01  
50%              1.000000                    0.000000 -3.722976e-02  
75%              1.000000                    0.000000  1.401617e-01  
max              3.000000                   60.000000  1.651857e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.965
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.813
Chunk:
Evaluation time span: 2013-06-16 00:00:00 2013-06-22 00:00:00
Model return rate:    0.166153846154
Original return rate: 0.166153846154
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-22 00:00:00
Model return rate:    0.11355815554
Original return rate: 0.21293471629
########################################
Timestamp: 2017-03-02T10:08:07GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'pca_comp_66', 'VIN_fracReturnedasm1DivTransactions', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_62', 'pca_comp_64', 'pca_comp_46', 'pca_comp_10', 'pca_comp_68', 'pca_comp_50', 'pca_comp_36', 'pca_comp_65', 'pca_comp_42', 'Arbitrated', 'pca_cluster_0_dist', 'JDPowersCat_COMPACTCAR', 'BuyerID_numReturnedNotNull', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_00', 'pca_comp_61', 'SellerID_numReturnedasm1', 'cluster_1_dist', 'pca_comp_57', 'pca_cluster_1_dist', 'CarYear', 'pca_comp_11', 'pca_comp_59', 'cluster_0_dist', 'pca_comp_58', 'BuyerID_fracReturnedasm1DivTransactions', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_21', 'pca_comp_48', 'pca_comp_39', 'pca_comp_54', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_37', 'pca_comp_06', 'SellerID_numDSEligible1', 'SellingLocation_numReturned1', 'pca_comp_08', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'pca_comp_24', 'DSEligible', 'pca_comp_22', 'pca_comp_40', 'CarMake_fracReturnedasm1DivTransactions', 'pca_comp_18', 'VIN_numReturned1', 'pca_comp_16', 'JDPowersCat_VAN', 'pca_comp_69', 'pca_comp_34', 'pca_comp_05', 'pca_comp_12', 'pca_comp_29', 'SellerID_numTransactions', 'JDPowersCat_numTransactions', 'JDPowersCat_fracReturnedasm1DivTransactions', 'JDPowersCat_SUV', 'pca_comp_56', 'pca_comp_47', 'BuyerID_numDSEligible1', 'SellingLocation_numDSEligible1', 'pca_comp_35', 'Mileage', 'pca_comp_03', 'pca_comp_72', 'pca_comp_27', 'pca_comp_20']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6020.000000             6020.000000   
mean                                0.054070                0.056146   
std                                 0.224052                0.230943   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm   pca_comp_66  VIN_fracReturnedasm1DivTransactions  
count   6020.000000  6.020000e+03                          6020.000000  
mean       0.067774  1.362857e-17                             0.068660  
std        0.251379  7.584248e-03                             0.251009  
min        0.000000 -2.202392e-01                             0.000000  
25%        0.000000 -8.749921e-04                             0.000000  
50%        0.000000 -2.065304e-04                             0.000000  
75%        0.000000  5.339752e-04                             0.000000  
max        1.000000  1.736245e-01                             1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.988
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.983
Chunk:
Evaluation time span: 2013-06-23 00:00:00 2013-06-29 00:00:00
Model return rate:    0.0
Original return rate: 0.203187250996
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-06-29 00:00:00
Model return rate:    0.0890447922288
Original return rate: 0.210649229332
########################################
Timestamp: 2017-03-02T10:08:14GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturned1', 'pca_comp_56', 'VIN_fracReturned1DivReturnedNotNull', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_36', 'pca_comp_61', 'VIN_numDSEligible1', 'BuyerID_numReturnedNotNull', 'pca_comp_44', 'cluster_1_dist', 'pca_comp_00', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_54', 'pca_comp_13', 'SellingLocation_fracReturned1DivReturnedNotNull', 'JDPowersCat_SPORTSCAR', 'pca_comp_07', 'pca_comp_60', 'pca_comp_32', 'BuyerID_numTransactions', 'BuyerID_numDSEligible1', 'pca_comp_43', 'pca_comp_11', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_21', 'SellerID_numReturned1', 'SellingLocation_fracReturnedasm1DivTransactions', 'SellingLocation_lat', 'pca_comp_67', 'VIN_numTransactions', 'pca_cluster_1_dist', 'pca_comp_52', 'pca_comp_47', 'pca_comp_48', 'pca_comp_01', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_50', 'BuyerID_numReturned1', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_49', 'JDPowersCat_numReturnedasm1', 'JDPowersCat_numReturnedNotNull', 'pca_comp_05', 'pca_comp_40', 'pca_comp_41', 'pca_comp_38', 'pca_comp_08', 'pca_comp_15', 'pca_comp_02', 'pca_comp_68', 'pca_comp_14', 'pca_comp_27', 'JDPowersCat_SUV', 'pca_comp_51', 'JDPowersCat_numReturned1', 'SellerID_numTransactions', 'pca_comp_20', 'JDPowersCat_fracDSEligible1DivTransactions', 'pca_comp_62', 'pca_comp_65', 'DSEligible', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_37', 'CarMake_numReturnedasm1', 'pca_cluster_0_dist', 'pca_comp_66', 'SellingLocation_numTransactions', 'BuyerID_fracReturnedasm1DivTransactions', 'JDPowersCat_numTransactions', 'pca_comp_69', 'CarYear', 'JDPowersCat_numDSEligible1', 'pca_comp_35', 'pca_comp_26', 'cluster_0_dist', 'SellerID_fracReturnedasm1DivTransactions', 'SellerID_numDSEligible1', 'pca_comp_55', 'LIGHT_N0G1Y2R3', 'pca_cluster', 'CarMake_fracReturned1DivReturnedNotNull', 'Returned_asm', 'MMR', 'CarMake_fracReturnedasm1DivTransactions', 'SellingLocation_numReturnedasm1', 'pca_comp_45', 'pca_comp_10', 'pca_comp_46', 'Autocheck_score', 'BuyerID_numReturnedasm1', 'JDPowersCat_fracReturnedasm1DivTransactions', 'pca_comp_59', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedasm1', 'pca_comp_33', 'pca_comp_22', 'pca_comp_31', 'LIGHTR', 'pca_comp_23', 'pca_comp_12', 'CarMake_numReturned1', 'JDPowersCat_PICKUP', 'pca_comp_25', 'SaleDate_decyear', 'Mileage', 'pca_comp_53']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             5937.000000                            5937.000000   
mean                 0.069564                               0.067065   
std                  0.254432                               0.247722   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

       VIN_numReturned1   pca_comp_56  VIN_fracReturned1DivReturnedNotNull  
count       5937.000000  5.937000e+03                          5937.000000  
mean           0.000842 -1.329633e-17                             0.000842  
std            0.029010  4.357245e-02                             0.029010  
min            0.000000 -2.964535e-01                             0.000000  
25%            0.000000 -6.923517e-03                             0.000000  
50%            0.000000 -5.427751e-04                             0.000000  
75%            0.000000  7.572756e-03                             0.000000  
max            1.000000  1.344847e+00                             1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.990
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.479
Chunk:
Evaluation time span: 2013-06-30 00:00:00 2013-07-06 00:00:00
Model return rate:    0.220708446866
Original return rate: 0.220708446866
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-06 00:00:00
Model return rate:    0.110810810811
Original return rate: 0.212121212121
########################################
Timestamp: 2017-03-02T10:08:21GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_67', 'VIN_numDSEligible1', 'DSEligible', 'pca_comp_00', 'BuyerID_numReturned1', 'pca_comp_64', 'VIN_numTransactions', 'pca_comp_42', 'pca_comp_65', 'pca_comp_63', 'BuyerID_numReturnedNotNull', 'cluster_0_dist', 'pca_comp_49', 'pca_comp_45', 'pca_comp_37', 'pca_comp_72', 'cluster', 'pca_comp_58', 'CarYear', 'pca_cluster', 'pca_comp_01', 'pca_comp_39', 'SellerID_numDSEligible1', 'pca_comp_31', 'pca_comp_35', 'pca_comp_34', 'SellerID_numTransactions', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_cluster_1_dist', 'SellerID_fracReturnedasm1DivTransactions', 'pca_comp_36', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_20', 'pca_comp_10', 'cluster_1_dist']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  Returned_asm  \
count                            3750.000000   3750.000000   
mean                                0.099956      0.088000   
std                                 0.297239      0.283333   
min                                 0.000000      0.000000   
25%                                 0.000000      0.000000   
50%                                 0.000000      0.000000   
75%                                 0.000000      0.000000   
max                                 1.000000      1.000000   

       VIN_numReturnedNotNull  VIN_fracReturned1DivReturnedNotNull  \
count             3750.000000                          3750.000000   
mean                 0.103733                             0.025067   
std                  0.305828                             0.156348   
min                  0.000000                             0.000000   
25%                  0.000000                             0.000000   
50%                  0.000000                             0.000000   
75%                  0.000000                             0.000000   
max                  2.000000                             1.000000   

       VIN_numReturned1  
count       3750.000000  
mean           0.025333  
std            0.158845  
min            0.000000  
25%            0.000000  
50%            0.000000  
75%            0.000000  
max            2.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.987
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.985
Chunk:
Evaluation time span: 2013-07-07 00:00:00 2013-07-13 00:00:00
Model return rate:    0.0
Original return rate: 0.2
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-13 00:00:00
Model return rate:    0.0919282511211
Original return rate: 0.20987654321
########################################
Timestamp: 2017-03-02T10:08:28GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_numReturnedNotNull', 'VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numDSEligible1', 'pca_comp_45', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_44', 'Mileage', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_40', 'SellerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_numReturnedNotNull', 'CarMake_numReturned1', 'pca_comp_38', 'pca_comp_53', 'CarMake_numReturnedNotNull', 'pca_comp_31', 'pca_comp_52', 'cluster_1_dist', 'pca_comp_39', 'CarMake_fracDSEligible1DivTransactions', 'cluster_0_dist', 'pca_comp_56', 'CarMake_fracReturnedasm1DivTransactions', 'pca_comp_22', 'CarMake_numDSEligible1', 'pca_cluster_0_dist', 'JDPowersCat_PICKUP', 'pca_comp_16', 'pca_comp_28', 'pca_cluster_1_dist', 'SellerID_fracReturnedasm1DivTransactions', 'CarMake_numTransactions', 'CarMake_numReturnedasm1', 'pca_comp_34', 'SellerID_fracDSEligible1DivTransactions', 'SellerID_numDSEligible1', 'pca_comp_59', 'pca_comp_19', 'SellerID_numReturnedNotNull', 'pca_comp_14', 'SellerID_numTransactions', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_33']
Summarize top 5 important features:
       VIN_numReturnedNotNull  VIN_fracReturnedNotNullDivDSEligible1  \
count             6574.000000                            6574.000000   
mean                 0.071342                               0.069364   
std                  0.257414                               0.252138   
min                  0.000000                               0.000000   
25%                  0.000000                               0.000000   
50%                  0.000000                               0.000000   
75%                  0.000000                               0.000000   
max                  1.000000                               1.000000   

       VIN_numDSEligible1   pca_comp_45  \
count         6574.000000  6.574000e+03   
mean             0.968969  9.178571e-17   
std              0.294911  1.240888e-01   
min              0.000000 -1.320551e+00   
25%              1.000000 -4.878065e-02   
50%              1.000000 -1.317990e-02   
75%              1.000000  1.669662e-02   
max              4.000000  2.122892e+00   

       SellerID_fracReturned1DivReturnedNotNull  
count                               6574.000000  
mean                                   0.047432  
std                                    0.128853  
min                                    0.000000  
25%                                    0.000000  
50%                                    0.000000  
75%                                    0.000000  
max                                    1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.993
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.802
Chunk:
Evaluation time span: 2013-07-14 00:00:00 2013-07-20 00:00:00
Model return rate:    0.244698205546
Original return rate: 0.244698205546
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-20 00:00:00
Model return rate:    0.120401337793
Original return rate: 0.215659712815
########################################
Timestamp: 2017-03-02T10:08:36GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'pca_comp_65', 'pca_cluster_1_dist', 'pca_comp_66', 'pca_comp_01', 'VIN_numDSEligible1', 'pca_comp_64', 'pca_cluster_0_dist', 'cluster_0_dist', 'BuyerID_numReturnedNotNull', 'VIN_numTransactions', 'pca_comp_59', 'cluster_1_dist', 'pca_comp_31', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_numReturned1', 'pca_comp_63', 'pca_comp_09', 'Mileage', 'cluster', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_55', 'pca_comp_56', 'pca_comp_52', 'pca_comp_00', 'pca_comp_36', 'SellerID_numDSEligible1', 'Arbitrated', 'pca_comp_12', 'pca_comp_33', 'pca_comp_05', 'pca_comp_08', 'pca_comp_51', 'MMR', 'Autocheck_score', 'pca_comp_42', 'SellerID_numTransactions', 'CarYear', 'pca_comp_37', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_16', 'pca_comp_46', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_47', 'pca_comp_24', 'JDPowersCat_numReturnedNotNull', 'pca_comp_53', 'BuyerID_numReturnedasm1', 'SellerID_numReturnedasm1', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_38', 'pca_comp_07', 'PSI', 'pca_comp_70', 'VIN_fracReturnedasm1DivTransactions', 'SalePrice', 'BuyerID_numTransactions', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_67', 'pca_comp_54', 'pca_comp_04', 'pca_comp_49']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6189.000000             6189.000000   
mean                                0.098872                0.104217   
std                                 0.295212                0.309769   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull  VIN_numReturned1  
count   6189.000000                          6189.000000       6189.000000  
mean       0.064631                             0.025852          0.026499  
std        0.245893                             0.158453          0.163616  
min        0.000000                             0.000000          0.000000  
25%        0.000000                             0.000000          0.000000  
50%        0.000000                             0.000000          0.000000  
75%        0.000000                             0.000000          0.000000  
max        1.000000                             1.000000          2.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.996
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.986
Chunk:
Evaluation time span: 2013-07-21 00:00:00 2013-07-27 00:00:00
Model return rate:    0.0
Original return rate: 0.269097222222
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-07-27 00:00:00
Model return rate:    0.106882591093
Original return rate: 0.22287321303
########################################
Timestamp: 2017-03-02T10:08:43GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_60', 'pca_comp_52', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_36', 'VIN_numReturned1', 'pca_comp_53', 'Returned_asm', 'pca_comp_37', 'SellingLocation_lon', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'DSEligible', 'pca_comp_01', 'VIN_numTransactions', 'SellerID_fracReturned1DivReturnedNotNull', 'BuyerID_numReturnedNotNull', 'pca_comp_66', 'BuyerID_numDSEligible1', 'pca_comp_50', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_56', 'pca_comp_05', 'SellerID_fracReturnedasm1DivTransactions', 'SellingLocation_fracReturned1DivReturnedNotNull', 'cluster_1_dist', 'pca_comp_34', 'BuyerID_numTransactions', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_00', 'pca_comp_48', 'CarMake_fracReturnedNotNullDivDSEligible1', 'SellingLocation_fracReturnedasm1DivTransactions', 'pca_comp_16', 'pca_comp_11', 'pca_comp_40', 'pca_comp_06', 'CarYear', 'pca_cluster_0_dist', 'pca_comp_29', 'pca_comp_57', 'SellingLocation_lat', 'pca_comp_38', 'cluster_0_dist', 'BuyerID_fracReturned1DivReturnedNotNull', 'SellingLocation_numTransactions', 'JDPowersCat_VAN', 'pca_comp_39', 'pca_comp_07', 'pca_comp_28', 'pca_comp_24', 'pca_comp_58', 'pca_comp_42', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_63', 'pca_comp_09', 'pca_comp_31', 'SellingLocation_numReturnedasm1', 'BuyerID_numReturnedasm1', 'pca_comp_10', 'pca_comp_68', 'LIGHT_N0G1Y2R3', 'pca_comp_33', 'pca_comp_21', 'pca_comp_49', 'pca_comp_23', 'SellingLocation_numReturnedNotNull', 'LIGHTR', 'pca_comp_27', 'pca_comp_03', 'SalePrice', 'Arbitrated', 'pca_comp_45', 'pca_comp_46', 'SellerID_fracDSEligible1DivTransactions', 'JDPowersCat_numReturnedNotNull', 'JDPowersCat_numDSEligible1', 'pca_comp_54', 'pca_comp_15', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_32', 'pca_comp_18', 'VIN_numDSEligible1', 'pca_comp_44', 'pca_comp_59', 'SellerID_numReturnedasm1', 'pca_comp_51', 'Autocheck_score', 'MMR', 'pca_comp_47', 'SellingLocation_numDSEligible1', 'pca_comp_41', 'pca_comp_04', 'Mileage', 'pca_comp_13', 'pca_comp_64', 'pca_comp_25', 'pca_comp_65', 'SellingLocation_numReturned1', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_61', 'pca_comp_55', 'JDPowersCat_numReturned1', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellerID_numReturned1', 'pca_comp_69', 'JDPowersCat_COMPACTCAR', 'VIN_fracDSEligible1DivTransactions']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5786.000000             5786.000000   
mean                                0.072531                0.076218   
std                                 0.256053                0.266021   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_numReturnedasm1  VIN_fracReturnedasm1DivTransactions   pca_comp_60  
count          5786.000000                          5786.000000  5.786000e+03  
mean              0.095230                             0.087885 -1.499662e-17  
std               0.300541                             0.278578  2.409827e-02  
min               0.000000                             0.000000 -5.944825e-01  
25%               0.000000                             0.000000 -2.675429e-03  
50%               0.000000                             0.000000 -1.020192e-03  
75%               0.000000                             0.000000  9.534016e-04  
max               2.000000                             1.000000  2.659562e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.994
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.362
Chunk:
Evaluation time span: 2013-07-28 00:00:00 2013-08-03 00:00:00
Model return rate:    0.256944444444
Original return rate: 0.256944444444
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-03 00:00:00
Model return rate:    0.127073113758
Original return rate: 0.226925459426
########################################
Timestamp: 2017-03-02T10:08:51GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_67', 'pca_comp_62', 'pca_comp_66', 'pca_comp_10', 'BuyerID_numReturnedNotNull', 'pca_comp_01', 'SellerID_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'pca_comp_04', 'VIN_numDSEligible1', 'SellerID_numReturnedasm1', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_64', 'pca_comp_58', 'pca_comp_09', 'Arbitrated', 'pca_cluster_0_dist', 'pca_comp_46', 'BuyerID_numReturned1', 'cluster_0_dist', 'pca_comp_29', 'pca_comp_61', 'pca_comp_25', 'BuyerID_fracReturned1DivReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_40', 'pca_comp_03', 'SalePrice', 'pca_comp_60', 'pca_comp_12', 'VIN_numTransactions', 'pca_comp_63', 'pca_comp_48', 'pca_comp_19', 'pca_comp_52', 'JDPowersCat_SPORTSCAR', 'pca_comp_06', 'MMR', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_33', 'pca_comp_53', 'JDPowersCat_numTransactions', 'pca_comp_34', 'pca_comp_08', 'pca_comp_30', 'pca_comp_56', 'JDPowersCat_numReturnedasm1', 'pca_comp_44', 'pca_comp_45', 'JDPowersCat_numReturnedNotNull', 'BuyerID_numDSEligible1', 'SellerID_numDSEligible1', 'SellerID_numTransactions', 'pca_cluster_1_dist', 'cluster_1_dist', 'CarMake_fracDSEligible1DivTransactions', 'SellerID_numReturned1', 'LIGHTR', 'pca_comp_13', 'SellerID_fracReturnedasm1DivTransactions', 'pca_comp_54', 'Mileage', 'pca_comp_26', 'pca_comp_69', 'pca_comp_02', 'pca_comp_00', 'pca_comp_43', 'LIGHT_N0G1Y2R3', 'JDPowersCat_numDSEligible1']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5913.000000             5913.000000   
mean                                0.098512                0.104854   
std                                 0.293687                0.310231   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull  \
count   5913.000000                          5913.000000   
mean       0.073398                             0.029427   
std        0.260810                             0.168512   
min        0.000000                             0.000000   
25%        0.000000                             0.000000   
50%        0.000000                             0.000000   
75%        0.000000                             0.000000   
max        1.000000                             1.000000   

       VIN_fracReturnedasm1DivTransactions  
count                          5913.000000  
mean                              0.077879  
std                               0.261841  
min                               0.000000  
25%                               0.000000  
50%                               0.000000  
75%                               0.000000  
max                               1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.997
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.995
Chunk:
Evaluation time span: 2013-08-04 00:00:00 2013-08-10 00:00:00
Model return rate:    0.0
Original return rate: 0.252727272727
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-10 00:00:00
Model return rate:    0.115942028986
Original return rate: 0.229556832932
########################################
Timestamp: 2017-03-02T10:08:59GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_numReturnedasm1', 'pca_comp_53', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_52', 'SellerID_numReturnedasm1', 'pca_comp_68', 'pca_comp_01', 'pca_comp_60', 'BuyerID_numReturnedNotNull', 'SellerID_numReturned1', 'pca_comp_38', 'pca_comp_58', 'Returned_asm', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'SellerID_fracReturned1DivReturnedNotNull', 'VIN_numTransactions', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_65', 'cluster', 'DSEligible', 'PSIEligible', 'VIN_numReturned1', 'Autocheck_score', 'LIGHT_N0G1Y2R3', 'pca_comp_25', 'pca_comp_15', 'pca_comp_63', 'LIGHTR', 'pca_comp_11', 'SellerID_fracDSEligible1DivTransactions', 'BuyerID_numReturned1', 'pca_comp_23', 'pca_comp_16', 'MMR', 'pca_comp_07', 'pca_comp_20', 'pca_comp_69', 'pca_cluster_1_dist', 'pca_comp_41', 'pca_comp_22', 'pca_comp_37', 'pca_comp_27', 'pca_comp_17', 'pca_comp_66', 'pca_comp_47', 'pca_comp_10', 'pca_comp_05', 'SellerID_numTransactions', 'Mileage', 'cluster_0_dist', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'CarMake_fracDSEligible1DivTransactions', 'BuyerID_fracReturned1DivReturnedNotNull']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6161.000000              6161.00000   
mean                                0.068035                 0.07304   
std                                 0.247775                 0.26394   
min                                 0.000000                 0.00000   
25%                                 0.000000                 0.00000   
50%                                 0.000000                 0.00000   
75%                                 0.000000                 0.00000   
max                                 1.000000                 2.00000   

       VIN_numReturnedasm1   pca_comp_53  VIN_fracReturned1DivReturnedNotNull  
count          6161.000000  6.161000e+03                          6161.000000  
mean              0.088297 -1.413570e-17                             0.003814  
std               0.288290  7.290043e-02                             0.060652  
min               0.000000 -4.557946e-01                             0.000000  
25%               0.000000 -9.764019e-03                             0.000000  
50%               0.000000 -2.045005e-03                             0.000000  
75%               0.000000  6.489227e-03                             0.000000  
max               3.000000  1.263895e+00                             1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.994
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.584
Chunk:
Evaluation time span: 2013-08-11 00:00:00 2013-08-17 00:00:00
Model return rate:    0.230529595016
Original return rate: 0.230529595016
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-17 00:00:00
Model return rate:    0.129733783277
Original return rate: 0.22966031483
########################################
Timestamp: 2017-03-02T10:09:07GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_66', 'VIN_fracReturnedasm1DivTransactions', 'SellerID_numReturnedasm1', 'pca_comp_61', 'VIN_numTransactions', 'pca_comp_01', 'BuyerID_numReturnedNotNull', 'pca_comp_33', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'VIN_numDSEligible1', 'pca_comp_34', 'pca_comp_56', 'BuyerID_numReturned1', 'pca_comp_12', 'pca_cluster_1_dist', 'Arbitrated', 'SellerID_fracReturned1DivReturnedNotNull', 'SellerID_numDSEligible1', 'cluster_1_dist', 'VIN_numReturned1', 'pca_comp_41', 'pca_comp_02', 'pca_comp_62', 'SellerID_numTransactions', 'pca_comp_19', 'pca_comp_04', 'pca_comp_00', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_37', 'pca_comp_21', 'pca_comp_47', 'pca_comp_65', 'pca_comp_63', 'pca_comp_60', 'pca_comp_38', 'pca_comp_51', 'VIN_numReturnedasm1', 'pca_comp_52', 'BuyerID_fracReturnedasm1DivTransactions', 'SellerID_numReturned1', 'LIGHTR', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_27', 'pca_comp_48', 'pca_comp_67', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_32', 'pca_comp_07', 'CarYear', 'cluster_0_dist', 'pca_comp_70', 'pca_comp_11', 'pca_comp_43', 'pca_comp_08', 'pca_comp_58', 'pca_comp_72', 'BuyerID_fracReturned1DivReturnedNotNull', 'SellerID_fracDSEligible1DivTransactions', 'SellerID_fracReturnedasm1DivTransactions', 'JDPowersCat_numReturnedNotNull', 'pca_comp_64', 'MMR', 'pca_comp_15', 'pca_comp_10', 'pca_comp_03', 'pca_comp_39', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_24', 'Autocheck_score', 'pca_comp_45', 'pca_comp_06', 'pca_comp_55', 'pca_comp_23', 'SaleDate_decyear', 'BuyerID_numDSEligible1', 'pca_comp_53', 'pca_comp_59', 'pca_comp_28', 'Mileage', 'JDPowersCat_numTransactions', 'pca_comp_30', 'pca_comp_22', 'CarMake_fracReturnedasm1DivTransactions', 'CarMake_fracDSEligible1DivTransactions']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  Returned_asm  \
count                            6503.000000   6503.000000   
mean                                0.100351      0.078887   
std                                 0.296334      0.269583   
min                                 0.000000      0.000000   
25%                                 0.000000      0.000000   
50%                                 0.000000      0.000000   
75%                                 0.000000      0.000000   
max                                 1.000000      1.000000   

       VIN_numReturnedNotNull  VIN_fracReturned1DivReturnedNotNull  \
count             6503.000000                          6503.000000   
mean                 0.106874                             0.027141   
std                  0.313424                             0.162152   
min                  0.000000                             0.000000   
25%                  0.000000                             0.000000   
50%                  0.000000                             0.000000   
75%                  0.000000                             0.000000   
max                  2.000000                             1.000000   

        pca_comp_66  
count  6.503000e+03  
mean  -2.052982e-18  
std    9.090636e-03  
min   -2.504469e-01  
25%   -7.261761e-04  
50%    6.435760e-05  
75%    7.087362e-04  
max    1.260821e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.997
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.982
Chunk:
Evaluation time span: 2013-08-18 00:00:00 2013-08-24 00:00:00
Model return rate:    0.0
Original return rate: 0.184568835098
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-24 00:00:00
Model return rate:    0.117827345479
Original return rate: 0.225209080048
########################################
Timestamp: 2017-03-02T10:09:15GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_60', 'pca_comp_35', 'pca_comp_49', 'pca_comp_34', 'DSEligible', 'pca_cluster_0_dist', 'Returned_asm', 'cluster_0_dist', 'pca_comp_58', 'pca_comp_63', 'SellingLocation_lon', 'VIN_numReturned1', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_39', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_11', 'pca_comp_66', 'VIN_numReturnedasm1', 'BuyerID_numReturnedNotNull', 'pca_comp_37', 'pca_comp_13', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_02', 'SellingLocation_lat', 'pca_comp_14', 'VIN_fracDSEligible1DivTransactions', 'PSIEligible', 'BuyerID_numReturned1', 'pca_comp_09', 'pca_comp_64', 'pca_comp_29', 'CarYear', 'pca_comp_19', 'SellerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_numReturnedasm1', 'SellingLocation_numTransactions', 'pca_comp_47', 'pca_comp_52', 'pca_comp_55', 'pca_comp_45', 'SellingLocation_numDSEligible1', 'pca_comp_08', 'pca_comp_27', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_10', 'VIN_numTransactions', 'pca_comp_18', 'SellerID_numReturnedasm1', 'SellingLocation_numReturnedasm1', 'pca_comp_61', 'pca_comp_01', 'pca_comp_28', 'SellerID_fracReturnedasm1DivTransactions', 'SalePrice', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_05', 'SellerID_fracDSEligible1DivTransactions', 'Mileage', 'VIN_numDSEligible1', 'pca_comp_24', 'pca_comp_36', 'SellingLocation_numReturnedNotNull']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6216.000000             6216.000000   
mean                                0.089125                0.095560   
std                                 0.280361                0.297817   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull   pca_comp_60   pca_comp_35  
count                          6216.000000  6.216000e+03  6.216000e+03  
mean                              0.006113  7.557322e-19 -2.120962e-17  
std                               0.076391  2.712373e-02  3.110510e-01  
min                               0.000000 -5.443008e-01 -1.538647e+00  
25%                               0.000000 -3.803259e-03 -1.799287e-01  
50%                               0.000000 -1.545376e-03 -2.196175e-02  
75%                               0.000000  1.652658e-03  1.388972e-01  
max                               1.000000  2.317003e-01  2.139217e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.994
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.562
Chunk:
Evaluation time span: 2013-08-25 00:00:00 2013-08-31 00:00:00
Model return rate:    0.225543478261
Original return rate: 0.225543478261
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-08-31 00:00:00
Model return rate:    0.129822968679
Original return rate: 0.22524219591
########################################
Timestamp: 2017-03-02T10:09:23GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_66', 'pca_comp_61', 'VIN_numReturned1', 'DSEligible', 'pca_comp_64', 'pca_comp_62', 'pca_comp_70', 'pca_comp_46', 'pca_comp_39', 'pca_comp_36', 'pca_comp_53', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_71', 'pca_comp_32', 'pca_comp_59', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_42', 'CarMake_numReturned1', 'pca_comp_44', 'pca_comp_50', 'pca_comp_56', 'BuyerID_numTransactions', 'pca_comp_11', 'BuyerID_numReturnedasm1', 'VIN_numReturnedasm1', 'pca_comp_72', 'CarMake_numReturnedasm1', 'pca_comp_49', 'pca_comp_37', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_35', 'SellerID_fracReturnedasm1DivTransactions', 'pca_comp_02', 'pca_comp_47', 'pca_comp_45', 'BuyerID_fracDSEligible1DivTransactions', 'CarMake_numReturnedNotNull', 'JDPowersCat_numDSEligible1', 'CarMake_numTransactions', 'SellingLocation_lon', 'SellerID_fracReturned1DivReturnedNotNull', 'cluster_1_dist', 'pca_comp_51', 'BuyerID_numDSEligible1', 'pca_comp_60', 'SellingLocation_fracReturned1DivReturnedNotNull', 'pca_comp_27', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'pca_comp_58', 'JDPowersCat_numReturned1', 'pca_comp_29', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_34', 'SellingLocation_numReturnedasm1', 'CarMake_numDSEligible1', 'SellingLocation_lat', 'SellingLocation_numReturned1', 'pca_comp_04', 'pca_comp_14', 'pca_comp_43', 'SalePrice', 'pca_comp_38', 'Mileage', 'JDPowersCat_numReturnedNotNull', 'JDPowersCat_numTransactions', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'pca_comp_03', 'pca_comp_09', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_01', 'SellingLocation_numReturnedNotNull', 'pca_comp_57', 'MMR', 'pca_comp_69', 'pca_comp_17', 'pca_comp_28', 'pca_cluster_0_dist', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_33', 'pca_comp_63', 'BuyerID_fracReturnedasm1DivTransactions', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_22', 'JDPowersCat_fracDSEligible1DivTransactions', 'SellerID_numReturned1', 'pca_comp_07', 'JDPowersCat_LUXURYCAR', 'pca_comp_05', 'pca_comp_19', 'JDPowersCat_PICKUP', 'pca_comp_41', 'pca_comp_67', 'SellerID_numReturnedNotNull', 'SaleDate_dow', 'SellingLocation_numDSEligible1', 'JDPowersCat_numReturnedasm1', 'pca_comp_15', 'cluster', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_55', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_24', 'pca_comp_20', 'pca_comp_21', 'CarMake_fracReturnedasm1DivTransactions', 'BuyerID_numReturnedNotNull', 'pca_comp_06', 'pca_comp_31', 'pca_comp_54', 'pca_comp_23', 'pca_comp_18', 'SellingLocation_fracDSEligible1DivTransactions', 'VIN_numTransactions', 'pca_comp_65', 'pca_comp_52', 'CarYear', 'pca_comp_08', 'SellerID_numReturnedasm1', 'SellerID_numTransactions', 'BuyerID_numReturned1', 'pca_comp_26', 'JDPowersCat_fracReturnedasm1DivTransactions', 'pca_comp_68']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6331.000000             6331.000000   
mean                                0.116990                0.125888   
std                                 0.316842                0.340677   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_66  
count   6331.000000                          6331.000000  6.331000e+03  
mean       0.091771                             0.031906 -1.117285e-17  
std        0.288725                             0.175090  9.898213e-03  
min        0.000000                             0.000000 -1.837539e-01  
25%        0.000000                             0.000000 -8.687123e-04  
50%        0.000000                             0.000000 -2.442378e-05  
75%        0.000000                             0.000000  6.976617e-04  
max        1.000000                             1.000000  2.311562e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.990
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.994
Chunk:
Evaluation time span: 2013-09-01 00:00:00 2013-09-07 00:00:00
Model return rate:    0.0
Original return rate: 0.193602693603
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-07 00:00:00
Model return rate:    0.1210496614
Original return rate: 0.222900573137
########################################
Timestamp: 2017-03-02T10:09:32GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'pca_comp_51', 'pca_comp_13', 'pca_comp_59', 'pca_comp_26', 'Returned_asm', 'pca_comp_66', 'DSEligible', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_27', 'pca_comp_58', 'pca_comp_57', 'pca_comp_15', 'cluster_1_dist', 'pca_comp_53', 'pca_comp_52', 'pca_comp_65', 'pca_cluster', 'pca_comp_41', 'pca_cluster_0_dist', 'pca_comp_47', 'BuyerID_numDSEligible1', 'pca_cluster_1_dist', 'cluster', 'BuyerID_numTransactions', 'SellingLocation_fracReturnedasm1DivTransactions', 'VIN_numTransactions', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_23', 'JDPowersCat_numReturnedasm1', 'pca_comp_08', 'pca_comp_56', 'pca_comp_11', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'pca_comp_48', 'SellingLocation_fracDSEligible1DivTransactions', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_40', 'pca_comp_67', 'pca_comp_09']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5026.000000             5026.000000   
mean                                0.096896                0.102268   
std                                 0.292860                0.308240   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturned1   pca_comp_51  
count                          5026.000000       5026.000000  5.026000e+03  
mean                              0.003880          0.003979  7.573970e-18  
std                               0.061772          0.062962  9.247145e-02  
min                               0.000000          0.000000 -4.871721e-01  
25%                               0.000000          0.000000 -1.715146e-02  
50%                               0.000000          0.000000  3.905734e-03  
75%                               0.000000          0.000000  2.302979e-02  
max                               1.000000          1.000000  1.228385e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.993
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.404
Chunk:
Evaluation time span: 2013-09-08 00:00:00 2013-09-14 00:00:00
Model return rate:    0.221621621622
Original return rate: 0.221621621622
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-14 00:00:00
Model return rate:    0.130556974962
Original return rate: 0.222792607803
########################################
Timestamp: 2017-03-02T10:09:40GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_64', 'pca_comp_60', 'pca_comp_66', 'VIN_numReturned1', 'pca_comp_61', 'pca_comp_47', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_11', 'VIN_numTransactions', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_55', 'Arbitrated', 'pca_comp_30', 'VIN_numReturnedasm1', 'pca_comp_54', 'pca_comp_00', 'pca_comp_48', 'pca_comp_52', 'pca_comp_28', 'pca_comp_26', 'pca_cluster_0_dist', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_24', 'pca_comp_18', 'BuyerID_numDSEligible1', 'VIN_numDSEligible1', 'pca_comp_41', 'pca_comp_14', 'pca_comp_56', 'CarMake_fracReturnedNotNullDivDSEligible1', 'cluster_1_dist', 'BuyerID_numTransactions', 'pca_comp_38', 'pca_comp_37', 'pca_comp_32', 'pca_comp_33', 'SellerID_numReturnedasm1', 'pca_comp_04', 'pca_comp_23', 'pca_comp_34', 'pca_comp_70', 'BuyerID_numReturned1', 'SellingLocation_fracReturned1DivReturnedNotNull', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_68', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_19', 'BuyerID_numReturnedNotNull', 'pca_comp_31', 'pca_comp_06', 'pca_cluster_1_dist', 'pca_comp_15', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_63', 'pca_comp_71', 'pca_comp_07', 'pca_comp_05', 'pca_comp_44', 'pca_comp_67', 'pca_comp_09', 'SellingLocation_numTransactions', 'pca_comp_29', 'SellingLocation_lat', 'cluster_0_dist', 'pca_comp_59', 'SellingLocation_numReturned1', 'pca_comp_27', 'JDPowersCat_SUV', 'SellerID_fracReturnedasm1DivTransactions', 'SellingLocation_fracReturnedasm1DivTransactions', 'pca_comp_46', 'CarMake_numDSEligible1', 'cluster', 'JDPowersCat_numReturnedNotNull', 'SellerID_numReturned1', 'pca_comp_39', 'CarMake_numTransactions', 'JDPowersCat_fracReturnedasm1DivTransactions', 'pca_comp_50', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'BuyerID_fracReturned1DivReturnedNotNull', 'JDPowersCat_fracDSEligible1DivTransactions', 'pca_comp_22', 'pca_comp_49', 'pca_comp_62', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_40', 'JDPowersCat_numReturned1', 'pca_comp_13', 'pca_comp_03', 'SellingLocation_numReturnedNotNull', 'pca_comp_25', 'pca_comp_58', 'JDPowersCat_PICKUP', 'LIGHTR', 'pca_comp_57', 'SellerID_numReturnedNotNull', 'pca_comp_51', 'pca_comp_43', 'JDPowersCat_numDSEligible1', 'SellerID_numDSEligible1', 'SaleDate_doy', 'pca_comp_69', 'pca_comp_17', 'pca_comp_53', 'BuyerID_numReturnedasm1', 'CarMake_numReturned1', 'MMR', 'pca_comp_01', 'DSEligible', 'SalePrice', 'Autocheck_score', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_10', 'pca_comp_42', 'pca_comp_08', 'JDPowersCat_numReturnedasm1', 'pca_comp_65', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_16', 'pca_comp_02', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_36', 'CarYear', 'JDPowersCat_numTransactions', 'CarMake_numReturnedNotNull']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6598.000000             6598.000000   
mean                                0.112029                0.120491   
std                                 0.310834                0.332926   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_64  
count   6598.000000                          6598.000000  6.598000e+03  
mean       0.086693                             0.029479  9.725777e-19  
std        0.281406                             0.168371  1.475371e-02  
min        0.000000                             0.000000 -5.620931e-01  
25%        0.000000                             0.000000 -7.779020e-04  
50%        0.000000                             0.000000  1.325380e-04  
75%        0.000000                             0.000000  9.685702e-04  
max        1.000000                             1.000000  1.351816e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.997
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.993
Chunk:
Evaluation time span: 2013-09-15 00:00:00 2013-09-21 00:00:00
Model return rate:    0.0
Original return rate: 0.235378031384
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-21 00:00:00
Model return rate:    0.12219033955
Original return rate: 0.223724516742
########################################
Timestamp: 2017-03-02T10:09:49GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'pca_comp_50', 'pca_comp_35', 'pca_comp_58', 'DSEligible', 'pca_comp_59', 'Returned_asm', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_53', 'pca_comp_62', 'VIN_numDSEligible1', 'BuyerID_numReturnedNotNull', 'SellerID_fracDSEligible1DivTransactions', 'pca_cluster_1_dist', 'pca_comp_48', 'pca_comp_00', 'BuyerID_numDSEligible1', 'pca_comp_66', 'BuyerID_numTransactions', 'pca_comp_02', 'pca_comp_65', 'pca_comp_40', 'SaleDate_day', 'VIN_fracDSEligible1DivTransactions', 'SellerID_fracReturnedasm1DivTransactions', 'SellerID_numReturnedNotNull', 'cluster_1_dist', 'pca_comp_21', 'SellerID_numTransactions', 'pca_cluster_0_dist', 'pca_comp_10', 'pca_comp_12', 'pca_comp_63', 'pca_comp_60', 'pca_comp_37', 'SaleDate_doy', 'SaleDate_decyear', 'pca_comp_30', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_01', 'cluster', 'VIN_numReturnedasm1', 'BuyerID_fracReturned1DivReturnedNotNull', 'SellerID_numDSEligible1', 'cluster_0_dist', 'pca_comp_51', 'pca_comp_34', 'pca_comp_25', 'Mileage']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6112.000000             6112.000000   
mean                                0.089469                0.095223   
std                                 0.281728                0.297423   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturned1   pca_comp_50  
count                          6112.000000       6112.000000  6.112000e+03  
mean                              0.004745          0.005236 -4.302395e-19  
std                               0.067523          0.074406  9.815489e-02  
min                               0.000000          0.000000 -6.552332e-01  
25%                               0.000000          0.000000 -2.470966e-02  
50%                               0.000000          0.000000  3.683626e-03  
75%                               0.000000          0.000000  3.089828e-02  
max                               1.000000          2.000000  1.511555e+00  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.993
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.497
Chunk:
Evaluation time span: 2013-09-22 00:00:00 2013-09-28 00:00:00
Model return rate:    0.198550724638
Original return rate: 0.198550724638
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-09-28 00:00:00
Model return rate:    0.128009719461
Original return rate: 0.222014374323
########################################
Timestamp: 2017-03-02T10:09:58GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'VIN_numReturnedNotNull', 'pca_comp_66', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_64', 'SellerID_numReturned1', 'SellingLocation_lat', 'pca_comp_60', 'VIN_numReturned1', 'SellingLocation_lon', 'pca_comp_00', 'pca_comp_61', 'SellerID_numReturnedasm1', 'BuyerID_numReturnedNotNull', 'pca_comp_25', 'pca_comp_38', 'pca_comp_31', 'VIN_fracReturnedasm1DivTransactions', 'VIN_numTransactions', 'pca_comp_01', 'BuyerID_fracReturned1DivReturnedNotNull', 'cluster', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_14', 'VIN_numDSEligible1', 'pca_comp_40', 'VIN_numReturnedasm1', 'pca_comp_51', 'SellingLocation_numReturnedasm1', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_52', 'pca_comp_02', 'pca_comp_59', 'pca_cluster_0_dist', 'pca_comp_33', 'SellingLocation_numReturned1', 'cluster_0_dist', 'pca_comp_06', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_50', 'pca_comp_47', 'SellingLocation_numReturnedNotNull', 'pca_comp_35', 'pca_comp_13', 'JDPowersCat_VAN', 'pca_comp_63', 'pca_comp_09', 'pca_comp_68', 'JDPowersCat_numReturnedasm1', 'pca_comp_18', 'VIN_fracDSEligible1DivTransactions', 'BuyerID_numDSEligible1', 'SellingLocation_numTransactions', 'pca_comp_53', 'pca_comp_27', 'pca_comp_69', 'LIGHTR', 'pca_comp_49', 'SellingLocation_numDSEligible1', 'SellerID_numReturnedNotNull', 'pca_comp_46', 'pca_comp_08', 'CarYear', 'pca_comp_32', 'pca_comp_11', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_12', 'pca_comp_37', 'pca_comp_29', 'pca_comp_10', 'BuyerID_numReturned1', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_48', 'SalePrice', 'pca_comp_62', 'pca_cluster_1_dist', 'pca_comp_45', 'pca_comp_24', 'pca_comp_71', 'pca_comp_16', 'JDPowersCat_fracDSEligible1DivTransactions', 'SellerID_numDSEligible1', 'Autocheck_score', 'SaleDate_doy', 'SaleDate_decyear', 'pca_comp_05', 'JDPowersCat_numReturned1', 'pca_comp_56', 'pca_comp_04', 'pca_comp_22', 'BuyerID_numTransactions', 'SellingLocation_fracReturned1DivReturnedNotNull', 'BuyerID_fracDSEligible1DivTransactions', 'pca_comp_26', 'pca_comp_42', 'pca_comp_43', 'pca_comp_58', 'CarMake_fracReturnedNotNullDivDSEligible1', 'Arbitrated', 'pca_comp_17', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'SaleDate_dow', 'pca_comp_07', 'JDPowersCat_numReturnedNotNull', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_30', 'pca_comp_65', 'pca_comp_15', 'pca_comp_54', 'SellerID_fracReturnedasm1DivTransactions']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  Returned_asm  \
count                            5919.000000   5919.000000   
mean                                0.118587      0.089035   
std                                 0.318143      0.284819   
min                                 0.000000      0.000000   
25%                                 0.000000      0.000000   
50%                                 0.000000      0.000000   
75%                                 0.000000      0.000000   
max                                 1.000000      1.000000   

       VIN_numReturnedNotNull   pca_comp_66  \
count             5919.000000  5.919000e+03   
mean                 0.128569 -1.726563e-17   
std                  0.343717  1.124832e-02   
min                  0.000000 -2.498508e-01   
25%                  0.000000 -1.036708e-03   
50%                  0.000000 -6.866202e-06   
75%                  0.000000  9.006167e-04   
max                  2.000000  1.205782e-01   

       VIN_fracReturned1DivReturnedNotNull  
count                          5919.000000  
mean                              0.029904  
std                               0.169341  
min                               0.000000  
25%                               0.000000  
50%                               0.000000  
75%                               0.000000  
max                               1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.997
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.988
Chunk:
Evaluation time span: 2013-09-29 00:00:00 2013-10-05 00:00:00
Model return rate:    0.0
Original return rate: 0.236694677871
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-05 00:00:00
Model return rate:    0.120741743932
Original return rate: 0.222978566829
########################################
Timestamp: 2017-03-02T10:10:08GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_numReturnedNotNull', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_60', 'VIN_numReturned1', 'pca_comp_24', 'pca_comp_14', 'pca_comp_58', 'DSEligible', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_38', 'Returned_asm', 'pca_comp_37', 'pca_comp_67', 'pca_comp_48', 'pca_comp_52', 'pca_comp_63', 'SellerID_fracReturnedNotNullDivDSEligible1', 'VIN_numTransactions', 'pca_comp_36', 'pca_comp_34', 'pca_comp_55', 'pca_comp_57', 'pca_comp_56', 'pca_comp_00', 'BuyerID_numReturned1', 'pca_comp_21', 'SaleDate_day', 'SaleDate_dow', 'SalePrice', 'SellerID_fracReturnedasm1DivTransactions', 'SaleDate_decyear', 'pca_comp_53', 'JDPowersCat_PICKUP', 'pca_cluster_0_dist', 'Autocheck_score', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_30', 'JDPowersCat_MIDSIZECAR', 'MMR', 'pca_comp_02', 'pca_comp_25', 'pca_comp_42', 'SellingLocation_numReturnedasm1', 'pca_comp_29', 'pca_comp_07', 'BuyerID_fracReturned1DivReturnedNotNull', 'VIN_fracDSEligible1DivTransactions', 'BuyerID_numReturnedasm1', 'pca_comp_18', 'SaleDate_doy', 'cluster_0_dist', 'SellingLocation_fracReturnedasm1DivTransactions', 'SellingLocation_numReturnedNotNull', 'pca_comp_22', 'pca_comp_03', 'pca_comp_10', 'pca_comp_19', 'pca_comp_41', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_12', 'SellingLocation_numDSEligible1', 'SellingLocation_fracReturned1DivReturnedNotNull', 'pca_comp_13', 'pca_comp_65', 'CarYear', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_68', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_50', 'SellerID_numTransactions', 'pca_comp_01', 'CarMake_fracReturnedasm1DivTransactions', 'VIN_numDSEligible1', 'pca_cluster_1_dist', 'pca_comp_46', 'BuyerID_numReturnedNotNull', 'pca_comp_40', 'pca_comp_27']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  \
count                            5821.000000   
mean                                0.098279   
std                                 0.291236   
min                                 0.000000   
25%                                 0.000000   
50%                                 0.000000   
75%                                 0.000000   
max                                 1.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturnedasm1  \
count                          5821.000000          5821.000000   
mean                              0.010222             0.108057   
std                               0.099950             0.316508   
min                               0.000000             0.000000   
25%                               0.000000             0.000000   
50%                               0.000000             0.000000   
75%                               0.000000             0.000000   
max                               1.000000             2.000000   

       VIN_numReturnedNotNull  VIN_fracReturnedasm1DivTransactions  
count             5821.000000                          5821.000000  
mean                 0.107542                             0.093211  
std                  0.314779                             0.279869  
min                  0.000000                             0.000000  
25%                  0.000000                             0.000000  
50%                  0.000000                             0.000000  
75%                  0.000000                             0.000000  
max                  3.000000                             1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.987
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.450
Chunk:
Evaluation time span: 2013-10-06 00:00:00 2013-10-12 00:00:00
Model return rate:    0.260740740741
Original return rate: 0.260740740741
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-12 00:00:00
Model return rate:    0.129939653494
Original return rate: 0.225186211675
########################################
Timestamp: 2017-03-02T10:10:17GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_64', 'DSEligible', 'pca_comp_61', 'pca_comp_59', 'VIN_numReturned1', 'VIN_numReturnedasm1', 'pca_comp_66', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_52', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_37', 'pca_comp_46', 'pca_comp_31', 'pca_comp_02', 'pca_comp_36', 'Arbitrated', 'pca_comp_38', 'cluster_1_dist', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_numTransactions', 'pca_comp_32', 'cluster_0_dist', 'SellingLocation_numDSEligible1', 'VIN_numTransactions', 'BuyerID_numDSEligible1', 'pca_comp_45', 'pca_comp_67', 'SellingLocation_fracReturned1DivReturnedNotNull', 'pca_comp_65', 'pca_comp_69', 'pca_comp_51', 'VIN_numDSEligible1', 'pca_cluster_0_dist', 'SellingLocation_numReturnedNotNull', 'pca_comp_20', 'pca_comp_26', 'pca_comp_01', 'pca_comp_06', 'SellingLocation_numReturned1', 'SellingLocation_numReturnedasm1', 'pca_comp_33', 'pca_comp_30', 'pca_comp_57', 'SellerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_numReturnedNotNull', 'pca_comp_34', 'pca_comp_12', 'SellerID_numDSEligible1', 'pca_comp_42', 'pca_comp_58', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellingLocation_numTransactions', 'pca_comp_47', 'SellingLocation_fracDSEligible1DivTransactions', 'JDPowersCat_SUV', 'SellerID_numReturnedNotNull', 'pca_comp_22', 'pca_comp_24', 'CarYear', 'pca_comp_10', 'pca_comp_23', 'CarMake_fracReturned1DivReturnedNotNull']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5924.000000             5924.000000   
mean                                0.115856                0.125591   
std                                 0.314463                0.337973   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_64  
count   5924.000000                          5924.000000  5.924000e+03  
mean       0.087441                             0.037643  2.415318e-17  
std        0.282504                             0.189682  1.449465e-02  
min        0.000000                             0.000000 -2.194927e-01  
25%        0.000000                             0.000000 -1.527965e-03  
50%        0.000000                             0.000000 -5.345464e-05  
75%        0.000000                             0.000000  1.274495e-03  
max        1.000000                             1.000000  2.330418e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.995
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.995
Chunk:
Evaluation time span: 2013-10-13 00:00:00 2013-10-19 00:00:00
Model return rate:    0.0
Original return rate: 0.280575539568
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-19 00:00:00
Model return rate:    0.123909411546
Original return rate: 0.228331018708
########################################
Timestamp: 2017-03-02T10:10:26GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_fracReturnedasm1DivTransactions', 'Returned_asm', 'pca_comp_56', 'DSEligible', 'pca_comp_53', 'pca_comp_59', 'VIN_numReturned1', 'pca_comp_63', 'pca_comp_49', 'pca_comp_15', 'pca_comp_34', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_numDSEligible1', 'pca_comp_57', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_37', 'pca_comp_13', 'BuyerID_numTransactions', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_66', 'PSI', 'VIN_numDSEligible1', 'pca_comp_04', 'CarMake_numReturnedNotNull', 'pca_comp_40', 'Arbitrated', 'pca_comp_48', 'pca_comp_67', 'pca_comp_02', 'CarMake_numReturned1', 'pca_comp_29', 'pca_comp_31', 'pca_comp_70', 'pca_comp_33', 'SellingLocation_numTransactions', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'SellingLocation_fracDSEligible1DivTransactions', 'VIN_numTransactions', 'SellingLocation_lat', 'CarMake_numTransactions', 'pca_comp_43', 'pca_comp_62', 'SellingLocation_numReturned1', 'pca_comp_65', 'pca_comp_01', 'CarMake_fracReturnedasm1DivTransactions', 'pca_comp_68', 'JDPowersCat_numTransactions', 'CarMake_fracReturnedNotNullDivDSEligible1', 'pca_comp_23', 'JDPowersCat_numDSEligible1', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_36', 'CarMake_numDSEligible1', 'JDPowersCat_numReturnedasm1', 'pca_comp_03', 'pca_comp_58', 'CarYear', 'CarMake_numReturnedasm1', 'pca_comp_61', 'SellingLocation_numReturnedasm1', 'JDPowersCat_SUV', 'MMR', 'JDPowersCat_SPORTSCAR', 'pca_comp_44', 'JDPowersCat_fracReturnedasm1DivTransactions', 'JDPowersCat_numReturned1', 'Mileage', 'pca_comp_07', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_cluster_0_dist', 'pca_comp_11', 'pca_comp_17', 'pca_cluster', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_54', 'JDPowersCat_numReturnedNotNull']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5737.000000             5737.000000   
mean                                0.090030                0.096915   
std                                 0.282151                0.302856   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturnedasm1  \
count                          5737.000000          5737.000000   
mean                              0.006014             0.100924   
std                               0.075322             0.306418   
min                               0.000000             0.000000   
25%                               0.000000             0.000000   
50%                               0.000000             0.000000   
75%                               0.000000             0.000000   
max                               1.000000             2.000000   

       VIN_fracReturnedasm1DivTransactions  
count                          5737.000000  
mean                              0.089550  
std                               0.277042  
min                               0.000000  
25%                               0.000000  
50%                               0.000000  
75%                               0.000000  
max                               1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.985
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.526
Chunk:
Evaluation time span: 2013-10-20 00:00:00 2013-10-26 00:00:00
Model return rate:    0.238993710692
Original return rate: 0.238993710692
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-10-26 00:00:00
Model return rate:    0.131817788919
Original return rate: 0.228981282602
########################################
Timestamp: 2017-03-02T10:10:35GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_63', 'pca_comp_67', 'pca_comp_60', 'VIN_numReturned1', 'pca_comp_61', 'Arbitrated', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'VIN_numTransactions', 'VIN_numDSEligible1', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_25', 'pca_comp_00', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_54', 'VIN_numReturnedasm1', 'BuyerID_numReturnedNotNull', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_42', 'pca_comp_46', 'pca_comp_05', 'pca_comp_40', 'pca_comp_06', 'pca_comp_30', 'pca_comp_34', 'MMR', 'pca_comp_59', 'pca_comp_53']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6152.000000             6152.000000   
mean                                0.130188                0.141905   
std                                 0.330630                0.359536   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                3.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_63  
count   6152.000000                          6152.000000  6.152000e+03  
mean       0.093791                             0.038064 -1.378770e-17  
std        0.291561                             0.190312  1.996559e-02  
min        0.000000                             0.000000 -4.822711e-01  
25%        0.000000                             0.000000 -3.113831e-03  
50%        0.000000                             0.000000  2.611920e-05  
75%        0.000000                             0.000000  3.063983e-03  
max        1.000000                             1.000000  2.100999e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.993
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.983
Chunk:
Evaluation time span: 2013-10-27 00:00:00 2013-11-02 00:00:00
Model return rate:    0.0
Original return rate: 0.236220472441
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-02 00:00:00
Model return rate:    0.125504073739
Original return rate: 0.22938106972
########################################
Timestamp: 2017-03-02T10:10:45GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_numReturnedasm1', 'VIN_fracReturnedasm1DivTransactions', 'DSEligible', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_63', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'pca_comp_38', 'pca_comp_51', 'pca_comp_55', 'pca_comp_69', 'VIN_numReturned1', 'pca_comp_40', 'pca_comp_54', 'VIN_numTransactions', 'pca_comp_57', 'pca_comp_58', 'pca_comp_33', 'pca_comp_48', 'pca_comp_03', 'pca_comp_70', 'pca_comp_26', 'pca_comp_59', 'pca_cluster_1_dist', 'pca_comp_15', 'BuyerID_numReturned1', 'pca_comp_65', 'BuyerID_numReturnedNotNull', 'cluster_1_dist', 'SellerID_numReturnedasm1', 'cluster_0_dist', 'pca_comp_01', 'SellerID_numReturned1', 'pca_comp_21', 'pca_comp_00', 'InLane', 'pca_comp_47', 'pca_cluster_0_dist', 'pca_comp_19', 'CarYear', 'pca_comp_44', 'pca_comp_50', 'pca_comp_64', 'pca_comp_34', 'pca_comp_72', 'pca_comp_62', 'pca_comp_18', 'BuyerID_numDSEligible1', 'pca_comp_27', 'pca_comp_24', 'pca_comp_10', 'pca_comp_17', 'pca_comp_73', 'pca_comp_32', 'pca_comp_13', 'CarMake_numReturned1', 'CarMake_numReturnedasm1', 'pca_comp_37', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_49', 'pca_comp_68', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_76', 'SaleDate_decyear', 'pca_comp_41', 'BuyerID_fracReturned1DivReturnedNotNull', 'SellerID_numDSEligible1', 'VIN_numDSEligible1', 'SellerID_fracReturned1DivReturnedNotNull', 'JDPowersCat_MIDSIZECAR', 'pca_comp_08', 'pca_comp_60', 'pca_comp_67', 'pca_comp_53', 'Mileage', 'pca_comp_56', 'pca_comp_29', 'pca_comp_52', 'pca_comp_11', 'pca_comp_30', 'MMR', 'BuyerID_numReturnedasm1', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_07', 'BuyerID_numTransactions', 'JDPowersCat_fracReturnedasm1DivTransactions', 'JDPowersCat_COMPACTCAR', 'pca_comp_06', 'pca_comp_04', 'pca_comp_25', 'Autocheck_score', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'JDPowersCat_LUXURYCAR', 'JDPowersCat_numTransactions', 'SellingLocation_numReturnedNotNull', 'pca_comp_45']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            5519.000000             5519.000000   
mean                                0.107477                0.114513   
std                                 0.306044                0.325777   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_numReturnedasm1  VIN_fracReturnedasm1DivTransactions   DSEligible  
count          5519.000000                          5519.000000  5519.000000  
mean              0.106179                             0.097457     0.906686  
std               0.312184                             0.290763     0.290898  
min               0.000000                             0.000000     0.000000  
25%               0.000000                             0.000000     1.000000  
50%               0.000000                             0.000000     1.000000  
75%               0.000000                             0.000000     1.000000  
max               2.000000                             1.000000     1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.994
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.658
Chunk:
Evaluation time span: 2013-11-03 00:00:00 2013-11-09 00:00:00
Model return rate:    0.179761904762
Original return rate: 0.179761904762
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-09 00:00:00
Model return rate:    0.129012393195
Original return rate: 0.226533679464
########################################
Timestamp: 2017-03-02T10:10:55GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_64', 'pca_comp_69', 'pca_comp_71', 'pca_comp_66', 'VIN_numReturned1', 'DSEligible', 'pca_comp_33', 'pca_comp_38', 'pca_cluster_0_dist', 'pca_comp_60', 'pca_comp_41', 'BuyerID_numReturnedNotNull', 'pca_comp_42', 'cluster_0_dist', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_57', 'pca_comp_09', 'Autocheck_score', 'VIN_numTransactions', 'BuyerID_numReturned1', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_10', 'pca_comp_52', 'PSI', 'cluster_1_dist', 'pca_comp_34', 'VIN_numReturnedasm1', 'pca_cluster_1_dist', 'pca_comp_49', 'pca_comp_30', 'pca_comp_54', 'pca_comp_35', 'pca_comp_65', 'pca_comp_51', 'pca_comp_46', 'pca_comp_00', 'Mileage', 'pca_comp_01', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_72', 'pca_comp_31', 'pca_comp_48', 'JDPowersCat_SPORTSCAR', 'BuyerID_numTransactions', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_07', 'CarYear', 'BuyerID_numReturnedasm1', 'pca_comp_67', 'VIN_fracDSEligible1DivTransactions', 'SellerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_70', 'JDPowersCat_numTransactions', 'pca_comp_06', 'pca_comp_37', 'pca_comp_74', 'pca_comp_43', 'pca_comp_08', 'pca_comp_40', 'pca_comp_75', 'MMR', 'SellingLocation_numReturnedNotNull', 'pca_comp_62', 'VIN_numDSEligible1', 'pca_comp_76', 'BuyerID_fracDSEligible1DivTransactions', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'pca_comp_21', 'BuyerID_numDSEligible1', 'pca_comp_45', 'CarMake_fracReturnedasm1DivTransactions', 'pca_comp_25', 'pca_comp_04', 'pca_comp_44', 'JDPowersCat_numDSEligible1', 'pca_comp_18', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_02', 'pca_comp_19', 'SellerID_numReturned1', 'pca_comp_14', 'SalePrice', 'pca_comp_77', 'pca_comp_22', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_13', 'pca_comp_58', 'pca_comp_05', 'pca_comp_32', 'pca_comp_55', 'pca_comp_50']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6090.000000             6090.000000   
mean                                0.138820                0.147619   
std                                 0.341631                0.362536   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_64  
count   6090.000000                          6090.000000  6.090000e+03  
mean       0.082430                             0.030706  1.381968e-17  
std        0.275042                             0.171580  3.148035e-02  
min        0.000000                             0.000000 -2.345634e-01  
25%        0.000000                             0.000000 -4.228208e-03  
50%        0.000000                             0.000000  1.266129e-03  
75%        0.000000                             0.000000  5.727146e-03  
max        1.000000                             1.000000  6.151397e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.989
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.976
Chunk:
Evaluation time span: 2013-11-10 00:00:00 2013-11-16 00:00:00
Model return rate:    0.0
Original return rate: 0.178717598909
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-16 00:00:00
Model return rate:    0.123407701937
Original return rate: 0.224253464316
########################################
Timestamp: 2017-03-02T10:11:06GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturnedasm1', 'pca_comp_64', 'VIN_fracReturnedasm1DivTransactions', 'DSEligible', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'Returned_asm', 'pca_comp_55', 'pca_comp_41', 'pca_comp_50', 'pca_comp_61', 'pca_comp_39', 'VIN_numReturned1', 'pca_comp_09', 'SellerID_numTransactions', 'pca_comp_06', 'pca_comp_68', 'pca_comp_70', 'SellerID_numDSEligible1', 'cluster_0_dist', 'pca_cluster_0_dist', 'pca_comp_00', 'BuyerID_numReturnedNotNull', 'BuyerID_fracDSEligible1DivTransactions', 'CarYear', 'pca_comp_37', 'pca_comp_58', 'CarMake_numReturnedasm1', 'CarMake_numReturnedNotNull', 'pca_comp_67', 'pca_comp_20', 'CarMake_numReturned1', 'pca_comp_13', 'BuyerID_numReturned1', 'pca_comp_48', 'CarMake_numDSEligible1', 'pca_comp_11', 'pca_comp_10', 'pca_comp_23', 'pca_comp_45', 'BuyerID_fracReturned1DivReturnedNotNull', 'VIN_numDSEligible1', 'pca_comp_36', 'VIN_numTransactions', 'pca_comp_16', 'pca_cluster_1_dist', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_62', 'CarMake_fracReturnedasm1DivTransactions', 'pca_comp_21', 'pca_comp_33', 'pca_comp_28', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_66', 'pca_comp_53', 'CarMake_fracDSEligible1DivTransactions', 'Mileage', 'pca_comp_05', 'pca_comp_04', 'pca_comp_71', 'CarMake_numTransactions', 'BuyerID_numTransactions', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_59', 'SellingLocation_lat', 'pca_comp_43', 'pca_comp_08', 'pca_comp_44', 'pca_comp_24', 'pca_comp_52', 'SaleDate_decyear', 'pca_comp_69', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_60', 'SellerID_fracReturnedasm1DivTransactions', 'SellerID_fracReturnedNotNullDivDSEligible1', 'SellingLocation_numTransactions', 'pca_comp_19', 'JDPowersCat_MIDSIZECAR', 'pca_comp_02', 'SalePrice', 'JDPowersCat_numTransactions', 'pca_comp_56', 'SellerID_numReturnedNotNull', 'ConditionReport', 'pca_comp_57', 'pca_comp_22', 'pca_comp_46', 'SellingLocation_fracReturnedasm1DivTransactions', 'Simulcast', 'pca_comp_29', 'pca_comp_18', 'Autocheck_score', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_12', 'cluster_1_dist', 'pca_comp_25', 'pca_comp_51', 'SellerID_numReturned1', 'BuyerID_numReturnedasm1']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6190.000000             6190.000000   
mean                                0.098613                0.107108   
std                                 0.292975                0.315483   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturnedasm1   pca_comp_64  
count                          6190.000000          6190.000000  6.190000e+03  
mean                              0.008078             0.103069  4.269339e-18  
std                               0.087695             0.311425  3.184858e-02  
min                               0.000000             0.000000 -5.841709e-01  
25%                               0.000000             0.000000 -4.342334e-03  
50%                               0.000000             0.000000 -1.965280e-03  
75%                               0.000000             0.000000  1.178312e-03  
max                               1.000000             2.000000  3.681335e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.988
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.597
Chunk:
Evaluation time span: 2013-11-17 00:00:00 2013-11-23 00:00:00
Model return rate:    0.183979974969
Original return rate: 0.183979974969
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-23 00:00:00
Model return rate:    0.126773296245
Original return rate: 0.222263450835
########################################
Timestamp: 2017-03-02T10:11:15GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'pca_comp_68', 'pca_comp_71', 'pca_comp_66', 'InLane', 'DSEligible', 'pca_comp_65', 'VIN_fracReturned1DivReturnedNotNull', 'SellerID_fracDSEligible1DivTransactions', 'Arbitrated', 'pca_comp_51', 'pca_comp_61', 'cluster_0_dist', 'pca_comp_22', 'VIN_numDSEligible1', 'pca_comp_37', 'pca_cluster_0_dist', 'VIN_numReturned1', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'BuyerID_fracDSEligible1DivTransactions', 'BuyerID_numReturnedNotNull', 'VIN_numReturnedasm1', 'pca_comp_03', 'pca_comp_16', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_14', 'pca_comp_54', 'pca_comp_26', 'pca_comp_00', 'pca_comp_64', 'SellingLocation_fracReturned1DivReturnedNotNull', 'cluster_1_dist', 'pca_comp_40', 'pca_comp_50', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_43', 'pca_comp_74', 'pca_comp_25', 'JDPowersCat_numReturnedasm1', 'pca_cluster_1_dist', 'pca_comp_76', 'pca_comp_33', 'pca_comp_49', 'SellerID_numReturnedasm1', 'CarMake_numReturnedNotNull', 'pca_comp_11', 'SellingLocation_lat', 'pca_comp_32', 'pca_comp_07', 'pca_comp_69', 'pca_comp_27', 'Mileage', 'CarMake_numTransactions', 'pca_comp_62', 'pca_comp_23', 'SellingLocation_numReturned1', 'CarMake_fracReturnedNotNullDivDSEligible1', 'SellingLocation_numReturnedasm1', 'JDPowersCat_numDSEligible1', 'SellerID_numReturned1', 'pca_comp_01', 'pca_comp_44', 'pca_comp_48', 'CarMake_numReturned1', 'pca_comp_52', 'SellerID_fracReturned1DivReturnedNotNull', 'SellingLocation_numReturnedNotNull', 'SellerID_numTransactions', 'pca_comp_59', 'SellingLocation_numDSEligible1', 'pca_comp_67', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_73', 'PSI', 'pca_comp_60', 'pca_comp_06', 'BuyerID_numReturned1', 'pca_comp_15', 'BuyerID_numTransactions']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6301.000000             6301.000000   
mean                                0.128868                0.137915   
std                                 0.330203                0.351674   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       Returned_asm   pca_comp_68   pca_comp_71  
count   6301.000000  6.301000e+03  6.301000e+03  
mean       0.081416 -1.407876e-17 -1.256098e-17  
std        0.273494  1.787217e-02  1.229425e-02  
min        0.000000 -3.729362e-01 -1.994148e-01  
25%        0.000000 -1.428422e-03 -1.757616e-03  
50%        0.000000  2.584383e-04 -1.246202e-04  
75%        0.000000  1.824388e-03  1.313047e-03  
max        1.000000  3.348512e-01  1.809703e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.990
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.970
Chunk:
Evaluation time span: 2013-11-24 00:00:00 2013-11-30 00:00:00
Model return rate:    0.0
Original return rate: 0.181818181818
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-11-30 00:00:00
Model return rate:    0.123988301707
Original return rate: 0.221296631655
########################################
Timestamp: 2017-03-02T10:11:26GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_63', 'VIN_numReturnedasm1', 'pca_comp_56', 'VIN_numReturned1', 'BuyerID_fracReturned1DivReturnedNotNull', 'Returned_asm', 'DSEligible', 'pca_comp_61', 'pca_comp_54', 'pca_comp_68', 'Autocheck_score', 'pca_comp_40', 'pca_comp_53', 'SalePrice', 'pca_comp_19', 'pca_comp_58', 'pca_comp_04', 'pca_comp_14', 'pca_comp_55', 'SellerID_numReturned1', 'pca_comp_02', 'pca_comp_72', 'pca_comp_75', 'SellerID_numTransactions', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_35', 'CarMake_numReturnedNotNull', 'pca_comp_25', 'pca_comp_60', 'CarMake_numTransactions', 'pca_comp_22', 'pca_comp_71', 'SellerID_numDSEligible1', 'VIN_numDSEligible1', 'Mileage', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'BuyerID_fracReturnedasm1DivTransactions', 'pca_comp_62', 'CarYear', 'pca_comp_08', 'pca_comp_26', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_28', 'pca_comp_41', 'SellingLocation_fracDSEligible1DivTransactions', 'SellerID_numReturnedasm1', 'pca_comp_31', 'pca_comp_11', 'pca_comp_64', 'VIN_numTransactions', 'SellerID_numReturnedNotNull', 'pca_comp_27', 'pca_comp_23', 'pca_comp_24', 'pca_comp_10', 'pca_comp_07', 'CarMake_numDSEligible1', 'pca_comp_29', 'JDPowersCat_SUV', 'pca_comp_59', 'SellingLocation_lon', 'pca_comp_49', 'pca_comp_69', 'pca_comp_52']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            3574.000000             3574.000000   
mean                                0.089955                0.098769   
std                                 0.279902                0.302122   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  \
count                          3574.000000   
mean                              0.006156   
std                               0.077327   
min                               0.000000   
25%                               0.000000   
50%                               0.000000   
75%                               0.000000   
max                               1.000000   

       VIN_fracReturnedasm1DivTransactions   pca_comp_63  
count                          3574.000000  3.574000e+03  
mean                              0.093593  1.419292e-17  
std                               0.284634  3.166459e-02  
min                               0.000000 -5.151490e-01  
25%                               0.000000 -5.959335e-03  
50%                               0.000000 -2.113078e-03  
75%                               0.000000  3.029562e-03  
max                               1.000000  1.830524e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.998
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.528
Chunk:
Evaluation time span: 2013-12-01 00:00:00 2013-12-07 00:00:00
Model return rate:    0.175675675676
Original return rate: 0.175675675676
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-07 00:00:00
Model return rate:    0.126465065078
Original return rate: 0.219345891598
########################################
Timestamp: 2017-03-02T10:11:36GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_64', 'pca_comp_52', 'pca_comp_65', 'pca_comp_69', 'VIN_numReturned1', 'BuyerID_fracReturned1DivReturnedNotNull', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_70', 'pca_comp_60', 'pca_comp_28', 'pca_comp_58', 'pca_comp_23', 'pca_comp_44', 'pca_comp_08', 'PSI', 'pca_comp_30', 'pca_comp_02', 'DSEligible', 'SellerID_numReturned1', 'SellerID_numReturnedasm1', 'pca_comp_09', 'pca_comp_38', 'PSIEligible', 'pca_comp_07', 'VIN_fracReturnedasm1DivTransactions', 'cluster_1_dist', 'InLane', 'pca_comp_18', 'pca_comp_27', 'CarMake_numDSEligible1', 'SellerID_numReturnedNotNull', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_66', 'VIN_numReturnedasm1', 'BuyerID_numTransactions', 'VIN_numDSEligible1', 'CarMake_numReturnedasm1', 'pca_comp_00', 'pca_comp_33', 'CarMake_numReturnedNotNull', 'pca_comp_11', 'pca_comp_42', 'VIN_numTransactions', 'CarMake_numReturned1', 'pca_comp_34', 'pca_comp_12', 'pca_comp_06', 'pca_comp_05', 'pca_comp_41', 'pca_comp_21', 'pca_comp_57', 'pca_comp_63', 'ConditionReport', 'pca_comp_37', 'pca_comp_15', 'Autocheck_score', 'pca_comp_76', 'SellerID_numTransactions', 'pca_comp_46', 'pca_comp_10', 'pca_comp_01', 'pca_comp_14', 'pca_comp_62', 'pca_comp_48', 'pca_comp_75', 'pca_cluster_1_dist', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_55', 'pca_comp_50', 'CarMake_numTransactions', 'BuyerID_numReturnedNotNull', 'pca_comp_19', 'pca_comp_61', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_03', 'pca_comp_54', 'pca_comp_35', 'pca_comp_51', 'SellingLocation_fracReturnedasm1DivTransactions', 'SellingLocation_fracReturned1DivReturnedNotNull', 'BuyerID_numReturned1', 'SalePrice', 'BuyerID_fracDSEligible1DivTransactions', 'CarMake_fracDSEligible1DivTransactions', 'pca_comp_67']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6150.000000             6150.000000   
mean                                0.119444                0.127154   
std                                 0.319437                0.337055   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                3.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_64  
count   6150.000000                          6150.000000  6.150000e+03  
mean       0.071057                             0.025637 -2.857075e-18  
std        0.256941                             0.157432  2.726085e-02  
min        0.000000                             0.000000 -2.677673e-01  
25%        0.000000                             0.000000 -3.248264e-03  
50%        0.000000                             0.000000  1.086404e-03  
75%        0.000000                             0.000000  4.289136e-03  
max        1.000000                             1.000000  6.152466e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.996
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.972
Chunk:
Evaluation time span: 2013-12-09 00:00:00 2013-12-14 00:00:00
Model return rate:    0.0
Original return rate: 0.170186335404
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-14 00:00:00
Model return rate:    0.121221525666
Original return rate: 0.217160841478
########################################
Timestamp: 2017-03-02T10:11:46GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'VIN_fracReturned1DivReturnedNotNull', 'VIN_numReturned1', 'Returned_asm', 'VIN_numTransactions', 'DSEligible', 'pca_comp_63', 'pca_comp_61', 'SellerID_fracDSEligible1DivTransactions', 'pca_comp_67', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_01', 'pca_comp_69', 'pca_comp_21', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_54', 'SellerID_fracReturnedasm1DivTransactions', 'pca_comp_14', 'BuyerID_numReturned1', 'VIN_numReturnedasm1', 'JDPowersCat_PICKUP', 'SellerID_numTransactions', 'SellerID_numDSEligible1', 'Simulcast', 'pca_comp_41', 'pca_comp_42', 'pca_comp_66', 'cluster_1_dist', 'pca_comp_18', 'InLane', 'pca_comp_04', 'pca_comp_00', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_12', 'BuyerID_numReturnedNotNull', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_38', 'pca_comp_39', 'pca_comp_57', 'SellerID_numReturned1', 'pca_comp_09', 'VIN_numDSEligible1', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_64', 'pca_comp_17', 'pca_comp_33', 'pca_comp_20', 'pca_comp_16', 'pca_comp_73']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6193.000000             6193.000000   
mean                                0.111427                0.118682   
std                                 0.310385                0.328396   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                2.000000   

       VIN_fracReturned1DivReturnedNotNull  VIN_numReturned1  Returned_asm  
count                          6193.000000       6193.000000   6193.000000  
mean                              0.007186          0.007751      0.078960  
std                               0.082779          0.087703      0.269698  
min                               0.000000          0.000000      0.000000  
25%                               0.000000          0.000000      0.000000  
50%                               0.000000          0.000000      0.000000  
75%                               0.000000          0.000000      0.000000  
max                               1.000000          1.000000      1.000000  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.990
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.650
Chunk:
Evaluation time span: 2013-12-15 00:00:00 2013-12-20 00:00:00
Model return rate:    0.144144144144
Original return rate: 0.144144144144
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-20 00:00:00
Model return rate:    0.122699030254
Original return rate: 0.212944175641
########################################
Timestamp: 2017-03-02T10:11:57GMT
`columns.pkl`, `transformer_scaler.pkl`, `transformer_pca.pkl`: Save column order and transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Save transformers.
`important_features` =
['VIN_fracReturnedNotNullDivDSEligible1', 'VIN_numReturnedNotNull', 'Returned_asm', 'VIN_fracReturned1DivReturnedNotNull', 'pca_comp_71', 'pca_comp_64', 'pca_comp_65', 'pca_comp_67', 'DSEligible', 'VIN_numReturned1', 'BuyerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_07', 'pca_comp_43', 'BuyerID_numReturnedasm1', 'BuyerID_fracDSEligible1DivTransactions', 'Mileage', 'Simulcast', 'VIN_numDSEligible1', 'SellingLocation_lon', 'VIN_fracReturnedasm1DivTransactions', 'pca_comp_37', 'pca_comp_52', 'pca_comp_13', 'pca_comp_10', 'pca_comp_25', 'pca_comp_48', 'pca_comp_02', 'BuyerID_fracReturnedasm1DivTransactions', 'CarYear', 'pca_comp_44', 'SellerID_fracReturnedNotNullDivDSEligible1', 'pca_comp_51', 'pca_comp_50', 'pca_comp_28', 'BuyerID_numTransactions', 'BuyerID_numDSEligible1', 'pca_comp_49', 'pca_comp_60', 'PSI', 'BuyerID_fracReturned1DivReturnedNotNull', 'pca_comp_36', 'pca_comp_47', 'pca_comp_05', 'pca_comp_53', 'JDPowersCat_fracReturnedNotNullDivDSEligible1', 'pca_comp_56', 'pca_comp_57', 'pca_comp_03', 'pca_comp_09', 'pca_comp_42', 'pca_comp_26', 'pca_comp_00', 'pca_comp_27', 'InLane', 'pca_comp_17', 'pca_comp_68', 'SellingLocation_numTransactions', 'pca_comp_34', 'pca_comp_38', 'CarMake_numReturnedasm1', 'pca_comp_40', 'pca_comp_30', 'LIGHTY', 'SellerID_fracReturnedasm1DivTransactions', 'SellingLocation_numDSEligible1', 'SellerID_fracReturned1DivReturnedNotNull', 'pca_comp_59', 'VIN_numTransactions', 'pca_comp_29', 'pca_comp_61', 'pca_comp_74', 'pca_comp_76', 'pca_comp_18', 'pca_comp_62', 'JDPowersCat_fracReturned1DivReturnedNotNull', 'CarMake_numReturnedNotNull', 'CarMake_fracReturnedasm1DivTransactions', 'BuyerID_numReturnedNotNull', 'BuyerID_numReturned1', 'MMR', 'pca_comp_16', 'JDPowersCat_COMPACTCAR', 'pca_comp_77', 'SellingLocation_numReturned1', 'CarMake_numDSEligible1', 'SellingLocation_numReturnedasm1', 'pca_comp_23', 'CarMake_numTransactions', 'pca_comp_12', 'pca_comp_35', 'SaleDate_dow', 'pca_comp_46', 'CarMake_numReturned1', 'pca_comp_33', 'SellingLocation_lat', 'SellerID_numTransactions', 'LIGHTR', 'Salvage', 'pca_comp_58', 'SellerID_numReturned1', 'pca_comp_45', 'pca_comp_32', 'SellingLocation_fracReturnedNotNullDivDSEligible1', 'pca_comp_22', 'pca_comp_41', 'CarMake_fracReturned1DivReturnedNotNull', 'pca_comp_55', 'pca_comp_08', 'pca_comp_14', 'SellingLocation_fracReturned1DivReturnedNotNull', 'JDPowersCat_fracReturnedasm1DivTransactions', 'SellingLocation_numReturnedNotNull', 'SellingLocation_fracDSEligible1DivTransactions', 'pca_comp_20', 'pca_comp_63', 'pca_comp_75', 'JDPowersCat_numReturned1', 'pca_comp_73', 'SalePrice', 'pca_comp_54', 'VIN_fracDSEligible1DivTransactions', 'pca_comp_01', 'pca_cluster']
Summarize top 5 important features:
       VIN_fracReturnedNotNullDivDSEligible1  VIN_numReturnedNotNull  \
count                            6082.000000             6082.000000   
mean                                0.181538                0.191878   
std                                 0.380963                0.402483   
min                                 0.000000                0.000000   
25%                                 0.000000                0.000000   
50%                                 0.000000                0.000000   
75%                                 0.000000                0.000000   
max                                 1.000000                3.000000   

       Returned_asm  VIN_fracReturned1DivReturnedNotNull   pca_comp_71  
count   6082.000000                          6082.000000  6.082000e+03  
mean       0.084512                             0.031020  1.316872e-17  
std        0.278177                             0.172569  1.295064e-02  
min        0.000000                             0.000000 -1.915956e-01  
25%        0.000000                             0.000000 -1.696117e-03  
50%        0.000000                             0.000000 -1.835708e-04  
75%        0.000000                             0.000000  1.324500e-03  
max        1.000000                             1.000000  2.197499e-01  
Progress: 20% 40% 60% 80% 100% 

Model score = 0.987
`features.pkl`, `estimator.pkl`: Save features and estimator.
`transformer_scaler`, `transformer_pca`: Load existing transformers.
`transformer_kmeans.pkl`, `transformer_kmeans_pca.pkl`: Load transformers.
`features.pkl`, `estimator.pkl`: Save features and estimator.
Model score = 0.962
Chunk:
Evaluation time span: 2013-12-22 00:00:00 2013-12-28 00:00:00
Model return rate:    0.0
Original return rate: 0.142857142857
Overall:
Evaluation time span: 2013-02-03 00:00:00 2013-12-28 00:00:00
Model return rate:    0.12117910191
Original return rate: 0.212037179685

# Overall return rate is Returned==1 / (Returned not null)
print("Plot performance of model")
path_plot_dir = os.path.join(path_data_dir, 'plot_performance_model')

# Plot overall return rate.
(xvals_orig, yvals_orig) = zip(*[(key[1], val) for (key, val) in sorted(retrates_orig_all.items())])
(xvals_modl, yvals_modl) = zip(*[(key[1], val) for (key, val) in sorted(retrates_modl_all.items())])
(xvals_diff, yvals_diff) = (xvals_orig, np.subtract(yvals_modl, yvals_orig))
plt.plot(
    xvals_orig, yvals_orig, marker='.', linestyle='-',
    color=sns.color_palette()[0], label='original return rate')
plt.plot(
    xvals_modl, yvals_modl, marker='.', linestyle='-',
    color=sns.color_palette()[1], label='model return rate')
plt.plot(
    xvals_diff, yvals_diff, marker='.', linestyle='--',
    color=sns.color_palette()[3], label='diff model-original')
plt.title("Return rates vs SaleDate")
plt.xlabel("SaleDate")
plt.ylabel("Overall return rate")
plt.legend(loc='upper left')
plt.tight_layout()
plt.savefig(
    os.path.join(path_plot_dir, 'perf-modl_returnrate_vs_saledate.png'),
    dpi=300)
plt.show()

# Plot return rate by buyer.
# Note: to get buyerid with col max: df_orig.loc[df_orig[col].argmax(), 'BuyerID']
buyerids = [
    '272356', # buyer with max BuyerID_numReturnedNotNull (max accepted DealShield)
    '328701', # buyer with max BuyerID_numReturned1 (max returns)
    '179863', # buyer with max BuyerID_fracReturned1DivReturnedNotNull (max return rate) and frequenly prohibited (36 weeks)
    '46857', # buyers frequently prohibited (38 weeks)
    '62851', # buyers frequently prohibited (36 weeks)
    '16640', # buyers frequently prohibited (36 weeks)
    '61773', # buyers frequently prohibited (35 weeks)
    '20718', # buyers frequently prohibited (35 weeks)
    '18584', # buyers frequently prohibited (34 weeks)
    '248009'] # buyers frequently prohibited (34 weeks)

for buyerid in buyerids:
    print('#'*40)
    print('BuyerID:', buyerid)

    fig = plt.figure()
    ax0 = fig.add_subplot(111)
    ax0.set_title(textwrap.dedent("""\
        Returns and return rates vs num transactions
        for BuyerID={buyerid}""".format(buyerid=buyerid)))
    ax0.set_xlabel('BuyerID_numTransactions')

    ax0.set_ylabel('BuyerID_numReturned1')
    lns0 = list()
    xvals_orig = df_orig.loc[df_orig['BuyerID']==buyerid, 'BuyerID_numTransactions'].values
    yvals0_orig = df_orig.loc[df_orig['BuyerID']==buyerid, 'BuyerID_numReturned1'].values
    lns0 += ax0.plot(
        xvals_orig, yvals0_orig, marker='.', linestyle='-', 
        color=sns.color_palette(palette='dark')[0], label='original BuyerID_numReturned1')
    xvals_modl = df_modl.loc[df_modl['BuyerID']==buyerid, 'BuyerID_numTransactions'].values
    yvals0_modl = df_modl.loc[df_modl['BuyerID']==buyerid, 'BuyerID_numReturned1'].values
    lns0 += ax0.plot(
        xvals_modl, yvals0_modl, marker='.', linestyle='-', 
        color=sns.color_palette(palette=None)[0], label='model BuyerID_numReturned1')
    ylim0 = (
        min(min(yvals0_orig), min(yvals0_modl)),
        max(max(max(yvals0_orig), max(yvals0_modl)), 1))
    ax0.set_ylim(ylim0)
    nticks = 6
    ax0.set_yticks(np.linspace(start=ylim0[0], stop=ylim0[1], num=nticks, endpoint=True))
    ax0.legend(lns0, [ln.get_label() for ln in lns0], loc='upper left')

    ax1 = ax0.twinx()
    ax1.set_ylabel(buyer_retrate)
    lns1 = list()
    yvals1_orig = df_orig.loc[df_orig['BuyerID']==buyerid, buyer_retrate].values
    lns1 += ax1.plot(
        xvals_orig, yvals1_orig, marker='.', linestyle='-', 
        color=sns.color_palette(palette='dark')[1], label='original '+buyer_retrate)
    yvals1_modl = df_modl.loc[df_modl['BuyerID']==buyerid, buyer_retrate].values
    lns1 += ax1.plot(
        xvals_modl, yvals1_modl, marker='.', linestyle='-', 
        color=sns.color_palette(palette=None)[1], label='model '+buyer_retrate)
    ylim1 = (0, 1)
    ax1.set_ylim(ylim1)
    ax1.set_yticks(np.linspace(start=ylim1[0], stop=ylim1[1], num=nticks, endpoint=True))
    ax1.legend(lns1, [ln.get_label() for ln in lns1], loc='lower right')
    plt.tight_layout()
    plt.savefig(
        os.path.join(path_plot_dir, 'perf-modl_returnrate_vs_transactions_for_'+buyerid+'.png'),
        dpi=300)
    plt.show(fig)

Plot performance of model

########################################
BuyerID: 272356

########################################
BuyerID: 328701

########################################
BuyerID: 179863

########################################
BuyerID: 46857

########################################
BuyerID: 62851

########################################
BuyerID: 16640

########################################
BuyerID: 61773

########################################
BuyerID: 20718

########################################
BuyerID: 18584

print

<function print>

del df_chunk, df_eval, df_modl, df_orig, df_test, df_train
gc.collect()

	BuyerID	SellerID	Mileage	VIN	SellingLocation	CarMake	SalePrice	CarYear	MMR	JDPowersCat	...	InLane	PSIEligible	SaleDate	Autocheck_score	ConditionReport	DSEligible	Returned
0	351757	441081	37055	1FAHP3M27CL374352	CAAI	FORD	13000	2012	13050	COMPACT CAR	...	0	1	13-07-25	1.000000	NaN	1	0.0
1	351757	442235	248	1G1JE6SB7D4103706	DETA	CHEVROLET	13900	2013	13600	COMPACT CAR	...	0	1	13-10-24	NaN	NaN	1	NaN
2	351757	436280	39627	1G1PG5SC4C7102274	CAAI	CHEVROLET	13800	2012	14050	MIDSIZE CAR	...	1	1	13-04-18	-2.666667	41	1	NaN
3	351757	436280	50773	1G1PG5SC5C7113820	CAAI	CHEVROLET	12900	2012	13150	MIDSIZE CAR	...	1	1	13-04-18	1.000000	31	1	NaN
4	351757	434202	54133	1GNKVGED4BJ301146	SVAA	CHEVROLET	17800	2011	18050	SUV	...	1	1	13-02-26	-1.400000	34	1	NaN

	Arbitrated	Autocheck_score	BuyerID_fracDSEligible1DivTransactions	BuyerID_fracReturned1DivReturnedNotNull	BuyerID_fracReturnedNotNullDivDSEligible1	BuyerID_fracReturnedasm1DivTransactions	BuyerID_numDSEligible1	BuyerID_numReturned1	BuyerID_numReturnedNotNull	BuyerID_numReturnedasm1	BuyerID_numTransactions	CarMake_fracDSEligible1DivTransactions	CarMake_fracReturned1DivReturnedNotNull	CarMake_fracReturnedNotNullDivDSEligible1	CarMake_fracReturnedasm1DivTransactions	CarMake_numDSEligible1	CarMake_numReturned1	CarMake_numReturnedNotNull	CarMake_numReturnedasm1	CarMake_numTransactions	CarYear	ConditionReport	DSEligible	InLane	JDPowersCat_COMPACTCAR	JDPowersCat_EXCLUDED	JDPowersCat_FULLSIZECAR	JDPowersCat_LUXURYCAR	JDPowersCat_MIDSIZECAR	JDPowersCat_PICKUP	JDPowersCat_SPORTSCAR	JDPowersCat_SUV	JDPowersCat_UNKNOWN	JDPowersCat_VAN	JDPowersCat_fracDSEligible1DivTransactions	JDPowersCat_fracReturned1DivReturnedNotNull	JDPowersCat_fracReturnedNotNullDivDSEligible1	JDPowersCat_fracReturnedasm1DivTransactions	JDPowersCat_numDSEligible1	JDPowersCat_numReturned1	JDPowersCat_numReturnedNotNull	JDPowersCat_numReturnedasm1	JDPowersCat_numTransactions	LIGHTG	LIGHTR	LIGHTY	LIGHT_N0G1Y2R3	MMR	Mileage	OVE	PSI	PSIEligible	SaleDate_day	SaleDate_decyear	SaleDate_dow	SaleDate_doy	SalePrice	Salvage	SellerID_fracDSEligible1DivTransactions	SellerID_fracReturned1DivReturnedNotNull	SellerID_fracReturnedNotNullDivDSEligible1	SellerID_fracReturnedasm1DivTransactions	SellerID_numDSEligible1	SellerID_numReturned1	SellerID_numReturnedNotNull	SellerID_numReturnedasm1	SellerID_numTransactions	SellingLocation_fracDSEligible1DivTransactions	SellingLocation_fracReturned1DivReturnedNotNull	SellingLocation_fracReturnedNotNullDivDSEligible1	SellingLocation_fracReturnedasm1DivTransactions	SellingLocation_lat	SellingLocation_lon	SellingLocation_numDSEligible1	SellingLocation_numReturned1	SellingLocation_numReturnedNotNull	SellingLocation_numReturnedasm1	SellingLocation_numTransactions	Simulcast	VIN_fracDSEligible1DivTransactions	VIN_fracReturned1DivReturnedNotNull	VIN_fracReturnedNotNullDivDSEligible1	VIN_fracReturnedasm1DivTransactions	VIN_numDSEligible1	VIN_numReturned1	VIN_numReturnedNotNull	VIN_numReturnedasm1	VIN_numTransactions
count	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000	278337.000000
mean	0.071316	1.198252	0.943581	0.107117	0.034551	0.063900	453.121827	1.252410	9.587022	12.178467	464.047884	0.946847	0.151509	0.028892	0.059119	9862.799344	83.209397	414.703399	669.181194	10448.771141	2007.407140	30.818285	0.944876	0.333700	0.125208	0.004800	0.011723	0.116345	0.221070	0.129473	0.032493	0.304778	0.001279	0.052832	0.946902	0.151373	0.028677	0.059050	25391.878335	242.096487	1102.838739	1535.855064	26685.636911	0.267977	0.217998	0.042592	1.007157	11388.524882	84228.544933	0.034034	0.306650	0.690659	15.596895	2013.526312	1.939318	193.630351	11973.232032	0.037555	0.944089	0.097179	0.039199	0.063685	494.175126	6.875238	32.126106	21.041644	508.341532	0.946303	0.106864	0.029225	0.059736	34.338469	-87.579034	8217.593320	116.052375	498.316293	630.544746	8732.085691	0.069459	0.944501	0.020815	0.072437	0.072814	0.981095	0.021837	0.079350	0.078822	1.038080
std	0.257353	1.715611	0.134171	0.254969	0.100233	0.138196	878.971988	6.337838	64.848962	37.019401	883.219232	0.033339	0.116539	0.029969	0.034017	10473.944904	125.020210	648.924844	792.745021	11147.755468	3.956124	5.129596	0.228222	0.471535	0.330955	0.069115	0.107637	0.320638	0.414968	0.335723	0.177306	0.460314	0.035741	0.223698	0.018795	0.101374	0.026053	0.019568	20343.521446	313.702224	1421.563472	1221.845520	21216.599910	0.442907	0.412887	0.201936	1.177273	9025.527367	54879.931708	0.181318	0.461104	0.462223	8.543311	0.259901	1.128183	95.123686	9083.860494	0.190118	0.158037	0.194698	0.108657	0.161155	1022.181316	41.863657	168.576836	64.173002	1040.906277	0.033447	0.136336	0.044604	0.037576	4.338165	9.354517	8746.189038	216.460544	910.684602	824.662178	9311.906488	0.254233	0.227985	0.141990	0.254448	0.255523	0.309004	0.150152	0.277162	0.274546	0.206104
min	0.000000	-32.599998	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	1936.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.500000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	2013.081967	0.000000	31.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	21.342331	-157.894707	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000
25%	0.000000	0.625000	0.962963	0.000000	0.000000	0.000000	34.000000	0.000000	0.000000	0.000000	38.000000	0.940678	0.000000	0.002083	0.049117	2039.000000	0.000000	6.000000	111.000000	2150.000000	2005.000000	30.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.939972	0.000000	0.002392	0.047186	8758.000000	0.000000	22.000000	521.000000	9265.000000	0.000000	0.000000	0.000000	0.000000	4775.000000	38778.000000	0.000000	0.000000	0.000000	8.000000	2013.303279	1.000000	112.000000	5600.000000	0.000000	0.959959	0.000000	0.000000	0.000000	10.000000	0.000000	0.000000	0.000000	12.000000	0.934462	0.000000	0.000000	0.032551	29.644441	-95.305058	1561.000000	0.000000	0.000000	60.000000	1636.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000
50%	0.000000	1.307692	0.993671	0.000000	0.000000	0.009146	104.000000	0.000000	0.000000	2.000000	112.000000	0.945253	0.185634	0.023974	0.058222	5616.000000	26.000000	109.000000	318.000000	5900.000000	2008.000000	30.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.945280	0.199100	0.023584	0.058522	20934.000000	94.000000	439.000000	1223.000000	22093.000000	0.000000	0.000000	0.000000	1.000000	9700.000000	79599.000000	0.000000	0.000000	1.000000	15.000000	2013.532787	2.000000	196.000000	10100.000000	0.000000	0.991255	0.000000	0.000000	0.014778	69.000000	0.000000	0.000000	2.000000	75.000000	0.947490	0.000000	0.000789	0.057628	34.294595	-86.444251	5179.000000	0.000000	2.000000	239.000000	5445.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000
75%	0.000000	2.250000	1.000000	0.000000	0.012658	0.055556	360.000000	0.000000	2.000000	10.000000	381.000000	0.956981	0.216374	0.052768	0.067680	14779.000000	109.000000	507.000000	950.000000	15642.000000	2011.000000	30.000000	1.000000	1.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.960821	0.222055	0.053513	0.068047	36523.000000	401.000000	1795.000000	2280.000000	38390.000000	1.000000	0.000000	0.000000	2.000000	15750.000000	114088.000000	0.000000	1.000000	1.000000	23.000000	2013.748634	3.000000	275.000000	16000.000000	0.000000	1.000000	0.128000	0.037333	0.052632	418.000000	1.000000	7.000000	12.000000	439.000000	0.968053	0.243865	0.052452	0.078275	38.399463	-79.894945	11784.000000	137.000000	592.000000	904.000000	12574.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000	0.000000	0.000000	0.000000	1.000000
max	1.000000	7.571429	1.000000	1.000000	1.000000	1.000000	5589.000000	157.000000	1452.000000	620.000000	5620.000000	1.000000	1.000000	1.000000	1.000000	44277.000000	569.000000	3241.000000	3364.000000	47072.000000	2014.000000	50.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	1.000000	0.437500	0.078305	0.500000	81571.000000	1311.000000	6165.000000	4571.000000	84831.000000	1.000000	1.000000	1.000000	3.000000	172000.000000	999999.000000	1.000000	1.000000	1.000000	31.000000	2013.994536	6.000000	365.000000	355000.000000	1.000000	1.000000	1.000000	1.000000	1.000000	7054.000000	725.000000	2736.000000	886.000000	7094.000000	1.000000	1.000000	0.219342	1.000000	47.425773	-71.163175	39569.000000	955.000000	4324.000000	3641.000000	41994.000000	1.000000	1.000000	1.000000	1.000000	1.000000	5.000000	3.000000	4.000000	3.000000	6.000000

Example development notebook for app_predict¶

Development below¶

Initialization¶

Imports¶

Globals¶

Extract, transform, and load¶

Exploratory data analysis¶

Heuristic model¶

Test heuristic¶

Pipeline model¶

Test pipeline model¶

Example development notebook for `app_predict`¶